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Relevant bibliographies by topics / Human action recognition

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Academic literature on the topic 'Human action recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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Journal articles on the topic "Human action recognition"

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Labana, Dileep, and Kirit Modi. "Human Action Recognition Using Dense Trajectories." Indian Journal Of Science And Technology 16, no. 43 (2023): 3846–53. http://dx.doi.org/10.17485/ijst/v16i43.2408.

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Abdelrazik, Mostafa A., Abdelhaliem Zekry, and Wael A. Mohamed. "Efficient Hybrid Algorithm for Human Action Recognition." Journal of Image and Graphics 11, no. 1 (2023): 72–81. http://dx.doi.org/10.18178/joig.11.1.72-81.

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Recently, researchers have sought to find the ideal way to recognize human actions through video using artificial intelligence due to the multiplicity of applications that rely on it in many fields. In general, the methods have been divided into traditional methods and deep learning methods, which have provided a qualitative leap in the field of computer vision. Convolutional neural network CNN and recurrent neural network RNN are the most popular algorithms used with images and video. The researchers combined the two algorithms to search for the best results in a lot of research. In an attemp

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Raju, Md Ismail Hossain, Sharmeen Sultana Ananna, Syed Shafiul Islam Meraz, Md Zakaria Azam, Seiichi Serikawa, and Md Atiqur Rahman Ahad. "Human Action Recognition: A Template Matching-based Approach." Journal of the Institute of Industrial Applications Engineers 5, no. 1 (2017): 15–23. http://dx.doi.org/10.12792/jiiae.5.15.

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Mori, Taketoshi, and Kousuke Tsujioka. "Human-Like Daily Action Recognition Model." Journal of Robotics and Mechatronics 17, no. 6 (2005): 672–80. http://dx.doi.org/10.20965/jrm.2005.p0672.

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This paper proposes a human-like action recognition model. When the model is implemented as a system, the system recognizes human actions similarly to human beings recognize. The recognition algorithm is constructed taking account of the following characteristics of human action recognition: simultaneous recognition, priority between actions, judgement fuzziness, multiple judge conditions for one action, and recognition ability from partial view of the body. The experiments based on a comparison with completed questionnaires demonstrated that the system recognizes human action the way like a h

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Mansouri, Amine, Toufik Bakir, and Smain Femmam. "Human Action Recognition with Skeleton and Infrared Fusion Model." Journal of Image and Graphics 11, no. 4 (2023): 309–20. http://dx.doi.org/10.18178/joig.11.4.309-320.

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Skeleton-based human action recognition conveys interesting information about the dynamics of a human body. In this work, we develop a method that uses a multi-stream model with connections between the parallel streams. This work is inspired by a state-of-the-art method called FUSIONCPA that merges different modalities: infrared input and skeleton input. Because we are interested in investigating improvements related to the skeleton-branch backbone, we used the Spatial-Temporal Graph Convolutional Networks (ST-GCN) model and an EfficientGCN attention module. We aim to provide improvements when

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Dhivya, Karunya S., and Kumar Krishna. "Human Activity Recognition Methods." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 1024–28. https://doi.org/10.35940/ijeat.E9771.069520.

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Human action in a video based application plays a significant role that alerts the researchers towards recognizing the motion of human. Other video applications also have video content extraction, summarization, and human computer interactions. The existing methods needs manual footnote of pertinent portion of actions of our interest. Recognition of human action can be done authentic without physical commentary of applicable parts of action of any one’s interest. In this paper we try to update the previous reviews on many ways of recognizing Human activities in videos that had different

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Et. al., Mihir Verma,. "Action Recognition Using Deep Learning And Cnn." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 11 (2021): 818–24. http://dx.doi.org/10.17762/turcomat.v12i11.5967.

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Automated action recognition using Deep learning and CNN is playing a vital role in today‘s day to day society, it may be video action recognitions through cctv, or it may be the smart homes. Now day’s human actions are used in many devices to control them like HoloLens VR, for that recognition of action is important that why video recognition. This Paper represents practical, reliable, and generic systems for video-based human action recognition, technology of CNN network is used to recognize different layers of the video images features. These features are obtained by extracting the features

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Pandey, Ritik, Yadnesh Chikhale, Ritik Verma, and Deepali Patil. "Deep Learning based Human Action Recognition." ITM Web of Conferences 40 (2021): 03014. http://dx.doi.org/10.1051/itmconf/20214003014.

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Human action recognition has become an important research area in the fields of computer vision, image processing, and human-machine or human-object interaction due to its large number of real time applications. Action recognition is the identification of different actions from video clips (an arrangement of 2D frames) where the action may be performed in the video. This is a general construction of image classification tasks to multiple frames and then collecting the predictions from each frame. Different approaches are proposed in literature to improve the accuracy in recognition. In this pa

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Liu, Lijue, Xiaoliang Lei, Baifan Chen, and Lei Shu. "Human Action Recognition Based on Inertial Sensors and Complexity Classification." Journal of Information Technology Research 12, no. 1 (2019): 18–35. http://dx.doi.org/10.4018/jitr.2019010102.

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In this article, a human action recognition technique based on complexity classification is proposed. Considering the features of human actions such as continuity, individuality, variety randomness, the demands for recognition of different types of actions are different, the problem of action recognition can be classified into simple action recognition and complex action recognition -- the classification criterions are given respectively. Meanwhile, the hardware design of data acquisition device is introduced and the angle variation is chosen to represent the user's body state changes. For sim

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Gaikwad, Suhani, Rutuja Ghodekar, Nikhil Gatkal, and Atharv Prayag. "Human Action Recognition using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1888–92. http://dx.doi.org/10.22214/ijraset.2023.51960.

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Abstract: The aim of this project is to recognize human actions for monitoring and security purposes. This project is mainly focused on building a system that is helpful for doctors to monitor patients .Human Action Recognition is required to recognize a set of human activities by training a supervised learning model and displaying the activity/action result as per the input action received. It has wide range of applications such as patient monitoring system, ATM/ Bank security system, etc. Human Action Recognition model can be mainly used for security and monitoring purposes. We can use vario

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Dissertations / Theses on the topic "Human action recognition"

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Oshin, Olusegun Temitope. "Human action recognition from relative motion." Thesis, University of Surrey, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549656.

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Stefic, Daria. "Learning saliency for human action recognition." Thesis, Queen Mary, University of London, 2016. http://qmro.qmul.ac.uk/xmlui/handle/123456789/23656.

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When we are looking at a visual stimuli, there are certain areas that stand out from the neighbouring areas and immediately grab our attention. A map that identi- es such areas is called a visual saliency map. As humans can easily recognize actions when watching videos, having their saliency maps available might be bene cial for a fully automated action recognition system. In this thesis we look into ways of learning to predict the visual saliency and how to use the learned saliency for action recognition. In the rst phase, as opposed to the approaches that use manually designed fea- tures for

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Liu, Chang. "Human motion detection and action recognition." HKBU Institutional Repository, 2010. http://repository.hkbu.edu.hk/etd_ra/1108.

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Umakanthan, Sabanadesan. "Human action recognition from video sequences." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/93749/1/Sabanadesan_Umakanthan_Thesis.pdf.

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This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of a

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Chakraborty, Bhaskar. "Model free approach towards human action recognition." Doctoral thesis, Universitat Autònoma de Barcelona, 2012. http://hdl.handle.net/10803/117657.

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La comprensio automatica de les accions humanes observades en sequencies d'imatges es molt important en el area de recerca de la Vision per Computador, amb aplicacions a gran escala en la vigilancia de video, analisi del moviment huma, interficies de realitat virtual, robots de navegacio, aixi com per al reconeixement, indexacio, i recuperacio de video. Aquesta tesi presenta una serie de tecniques per resoldre el problema del reconeixement de les accions humanes en video. El nostre primer enfocament cap a aquesta tema es basa en la optimitzacio d'un model probabilistic de les parts del co

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Wang, Xuelu. "Human Action Recognition from Gradient Boundary Histograms." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35931.

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This thesis presents a framework for automatic recognition of human actions in un- controlled, realistic video data with fixed cameras, such as surveillance videos. In this thesis, we divide human action recognition into three steps: description, representation, and classification of local spatio-temporal features. The bag-of-features model was used to build the classifier. Fisher Vectors were also studied. We focus on the potential of the methods, with the joint optimization of two constraints: the classification precision and its efficiency. On the performance side, a new local descriptor,

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Mejia, Maria Helena. "Human Action Recognition on Videos: Different Approaches." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/265361.

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The goal of human action recognition on videos is to determine in an automatic way what is happening in a video. This work focuses on providing an answer to this question: given consecutive frames from a video where a person or persons are doing an action, is an automatic system able to recognize the action that is going on for each person? Seven approaches have been provided, most of them based on an alignment process in order to find a measure of distance or similarity for obtaining the classification. Some are based on fluents that are converted to qualitative sequences of Allen relations t

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Megrhi, Sameh. "Spatio-temporal descriptors for human action recognition." Thesis, Paris 13, 2014. http://www.theses.fr/2014PA131046/document.

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L'analyse et l’interprétation de contenus visuels et plus particulièrement la vidéo est un domaine de recherche de plus en plus attractif en raison du nombre important d'applications telles que la vidéo-surveillance, le résumé de films, l'indexation, les jeux vidéo, la robotique et la domotique. Dans cette thèse nous nous intéressons à la détection et à la reconnaissance d'actions humaines dans des séquences vidéo. Pour la partie détection des actions, nous avons introduit deux approches basées sur les points d'intérêts locaux. La première proposition est une méthode simple et efficace qui vis

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Carmona, Leyva Josep Maria. "A template based approach for human action recognition." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/619801.

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Visual analysis of human movements concerns the understanding of human activities from image sequences. The goal of the action/gesture recognition is to recognize the label that corresponds to an action or gesture made by a human in a sequence of images. To solve this problem, the researchers have proposed solutions that range from object recognition techniques, to speech recognition techniques, face recognition or brain function . The techniques presented in this thesis, are related to a set of techniques that condense a video sequence into a template that retain important information to ac

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Boyraz, Hakan. "Human Action Localization and Recognition in Unconstrained Videos." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5910.

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As imaging systems become ubiquitous, the ability to recognize human actions is becoming increasingly important. Just as in the object detection and recognition literature, action recognition can be roughly divided into classification tasks, where the goal is to classify a video according to the action depicted in the video, and detection tasks, where the goal is to detect and localize a human performing a particular action. A growing literature is demonstrating the benefits of localizing discriminative sub-regions of images and videos when performing recognition tasks. In this thesis, we addr

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Books on the topic "Human action recognition"

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Wang, Jiang, Zicheng Liu, and Ying Wu. Human Action Recognition with Depth Cameras. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04561-0.

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service), SpringerLink (Online, and COST Action 2102 International Conference (2007 : Patra, Greece), eds. Verbal and nonverbal features of human-human and human-machine interaction: COST Action 2102 International Conference, Patras, Greece, October 29-31, 2007 : revised papers. Springer, 2008.

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David, Hutchison. Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction: COST Action 2102 International Conference, Patras, Greece, October 29-31, 2007. Revised Papers. Springer Berlin Heidelberg, 2008.

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Shirshov, Vladimir. Psychological readiness for actions in emergency situations. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/993543.

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The tutorial covers the issues of psychological competence and the willingness to act in emergency situations. Examines the theoretical and practical aspects of recognition, evaluation and prevention of emergency situations in the contemporary human environment. Meets the requirements of Federal state educational standards of higher education of the last generation. Can be used in the preparation of undergraduate students for 44.03.01 direction of preparation "Pedagogical education" (an optional component) in the conditions of multilevel training of future teachers and can also be

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Liu, Zicheng, Ying Wu, and Jiang Wang. Human Action Recognition with Depth Cameras. Springer London, Limited, 2014.

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De, Sourav, and Paramartha Dutta. Computational Intelligence for Human Action Recognition. Taylor & Francis Group, 2022.

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De, Sourav, and Paramartha Dutta. Computational Intelligence for Human Action Recognition. Taylor & Francis Group, 2020.

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Liu, Zicheng, Ying Wu, and Jiang Wang. Human Action Recognition with Depth Cameras. Springer, 2014.

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De, Sourav, and Paramartha Dutta. Computational Intelligence for Human Action Recognition. Taylor & Francis Group, 2020.

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De, Sourav, and Paramartha Dutta. Computational Intelligence for Human Action Recognition. Taylor & Francis Group, 2020.

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Book chapters on the topic "Human action recognition"

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He, Yun, Soma Shirakabe, Yutaka Satoh, and Hirokatsu Kataoka. "Human Action Recognition Without Human." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49409-8_2.

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Jia, Chengcheng, Wei Pang, and Yun Fu. "Multimodal Action Recognition." In Human Activity Recognition and Prediction. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_4.

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Yin, Dong, Yu-Qing Miao, Kang Qiu, and An Wang. "Study on Human Body Action Recognition." In Biometric Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97909-0_10.

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Jia, Chengcheng, Yu Kong, Zhengming Ding, and Yun Fu. "RGB-D Action Recognition." In Human Activity Recognition and Prediction. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_5.

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Kong, Yu, and Yun Fu. "Action Recognition and Human Interaction." In Human Activity Recognition and Prediction. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_2.

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Pravin Dhulekar, S. T. Gandhe, Harshada Chitte, and Komal Pardeshi. "Human Action Recognition: An Overview." In Proceedings of the International Conference on Data Engineering and Communication Technology. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1675-2_48.

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Fu, Meixia, Na Chen, Zhongjie Huang, et al. "Human Action Recognition: A Survey." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7123-3_9.

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Srivastava, Gaurav, Johnny Park, Avinash C. Kak, Birgi Tamersoy, and J. K. Aggarwal. "Multi-camera Human Action Recognition." In Computer Vision. Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_776.

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Singh, Dushyant Kumar. "Human Action Recognition in Video." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3140-4_6.

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Mazzeo, Pier Luigi, Paolo Spagnolo, Matteo Fasano, and Cosimo Distante. "Human Action Recognition with Transformers." In Image Analysis and Processing – ICIAP 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06433-3_20.

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Conference papers on the topic "Human action recognition"

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Kang, Naixin, Pengfei Yi, Qingshu Geng, Jing Dong, Rui Liu, and Ling Wang. "SC-LSTM Based Human Action Recognition." In 2024 9th International Conference on Signal and Image Processing (ICSIP). IEEE, 2024. http://dx.doi.org/10.1109/icsip61881.2024.10671561.

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Dwivedi, Ayushi, Mohd Shuaib, Aditya Joshi, Manoj Diwakar, Prabhishek Singh, and Amit Kumar Mishra. "Deep Learning Enabled Human Action Recognition." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911226.

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Sjarif, Nilam Nur Amir, and Siti Mariyam Shamsuddin. "Human action invarianceness for human action recognition." In 2015 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). IEEE, 2015. http://dx.doi.org/10.1109/skima.2015.7400036.

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Iosifidis, Alexandros, Anastasios Tefas, and Ioannis Pitas. "Active classification for human action recognition." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738669.

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Tanigawa, Risako, and Yasunori Ishii. "Hear-Your-Action: Human Action Recognition by Ultrasound Active Sensing." In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10447130.

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Jaouedi, Neziha, Noureddine Boujnah, Oumayma Htiwich, and Med Salim Bouhlel. "Human action recognition to human behavior analysis." In 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT). IEEE, 2016. http://dx.doi.org/10.1109/setit.2016.7939877.

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Jaouedi, Neziha, Noureddine Boujnah, Oumayma Htiwich, and Med Salim Bouhlel. "Human action recognition to human behavior analysis." In 2017 International Conference on Information and Digital Technologies (IDT). IEEE, 2017. http://dx.doi.org/10.1109/dt.2017.8012111.

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Xiao, Qinkun, and Yang Si. "Human action recognition using autoencoder." In 2017 3rd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2017. http://dx.doi.org/10.1109/compcomm.2017.8322824.

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Chen, Chong, Bo He, and Heng Zhang. "Review on Human Action Recognition." In 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC). IEEE, 2017. http://dx.doi.org/10.1109/icctec.2017.00026.

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Yousefi, Bardia, Chu Kiong Loo, and Ali Memariani. "Biological inspired human action recognition." In 2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS). IEEE, 2013. http://dx.doi.org/10.1109/riiss.2013.6607930.

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Reports on the topic "Human action recognition"

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Basu, Saikat, Malcolm Stagg, Robert DiBiano, Manohar Karki, and Supratik Mukhopadhyay. Human Action Recognition in Surveillance Videos using Abductive Reasoning on Linear Temporal Logic. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada586486.

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Pasupuleti, Murali Krishna. Empathetic AI in Action: Transforming Customer Service with Emotional Intelligence. National Education Services, 2025. https://doi.org/10.62311/nesx/rr725.

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Abstract: This article explores the transformative impact of Emotionally Intelligent AI on customer service, focusing on how AI systems are designed to understand and respond to human emotions with empathy and precision. It delves into the core technologies, such as sentiment analysis, emotion recognition models, and reinforcement learning, that enable AI to provide emotionally aware interactions. Practical applications are discussed, including AI-powered customer support, personalized experiences, and crisis management solutions. The Article also covers the psychological foundations of AI-dri

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Sripad, Pooja. Exploring barriers and enablers of service provision for survivors of human trafficking in the Bay Area: An action research study. Population Council, 2021. http://dx.doi.org/10.31899/sbsr2021.1067.

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Despite increasing recognition of public health and rights issues associated with human trafficking globally and in the United States following the Trafficking Victims Protection Act of 2000, there has been limited research on how to systematically strengthen service access for survivors of sex and labor trafficking. The experience of service providers may provide insight into how trafficking survivor responses and service networks function in California’s Bay Area. This study explores provider perspectives on existing service networks and collaboration dynamics, including the barriers to and

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Gordon, Dalia, Ke Dong, and Michael Gurevitz. Unexpected Specificity of a Sea Anemone Small Toxin for Insect Na-channels and its Synergic Effects with Various Insecticidal Ligands: A New Model to Mimic. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7697114.bard.

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Motivated by the high risks to the environment and human health imposed by the current overuse of chemical insecticides we offer an alternative approach for the design of highly active insect-selective compounds that will be based on the ability of natural toxins to differentiate between insect and mammalian targets. We wish to unravel the interacting surfaces of insect selective toxins with their receptor sites on voltage-gated sodium channels. In this proposal we put forward two recent observations that may expedite the development of a new generation of insect killers that mimic the highly

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Ambekar, Neelima, Divya Jain, Vishal Patel, Arvind Sakat, Abhishek Shah, and Nagma Shah. Exploring Education's Role in Sustainable Urbanisation through PUKAR's Youth Fellowship Program. Indian Institute for Human Settlements, 2023. http://dx.doi.org/10.24943/tesf1707.2024.

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This research explores the potential of the Youth Fellowship Program (YFP) as a supplementary urban educational intervention to formal higher education. The findings highlight the significance of the YFP as a crucial element in enabling research-oriented, problem-focused and action-oriented urban education. Such an approach complements formal urban higher education and addresses existing gaps, making contemporary urban education more responsive to the challenges faced in urban settings. Firstly, the programme recognises critical gaps in the contemporary higher education curriculum, ensuring th

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Gurevitz, Michael, William A. Catterall, and Dalia Gordon. Learning from Nature How to Design Anti-insect Selective Pesticides - Clarification of the Interacting Face between Insecticidal Toxins and their Na-channel Receptors. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7697101.bard.

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Structural details on the interacting faces of toxins and sodium channels (Navs), and particularly identification of elements that confer specificity for insects, are difficult to approach and require suitable experimental systems. Therefore, natural toxins capable of differential recognition of insect and mammalian Navs are valuable leads for design of selective compounds in insect control. We have characterized several scorpion toxins that vary in preference for insect and mammalian Navs, and identified residues important for their action. However, despite many efforts worldwide, only little

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Generalao, Ian Nicole, Jennylyn Balaoro, Pauline Joy Lorenzo, and John Paolo Rivera. Examining the Effects of Technical Vocational Education and Training (TVET) on Employment Outcomes in the Philippines. Philippine Institute for Development Studies, 2025. https://doi.org/10.62986/dp2025.08.

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Human capital theory suggests that accumulating skills through training leads to higher productivity and better employment outcomes. However, empirical evidence, including data from the Philippines, shows mixed and limited results regarding the impact of technical and vocational education and training (TVET). Using labor force and graduate tracer surveys, this study examined whether pursuing TVET improves labor force participation (LFP), employment, and job quality, while considering variations across education levels and program characteristics by applying probabilistic regression and treatme

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Who Owns the Land in Africa? Formal recognition of community-based land rights in Sub-Saharan Africa. Rights and Resources Initiative, 2015. http://dx.doi.org/10.53892/wlvi2246.

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The question of who owns the world’s lands and natural resources is a major source of contestation around the globe, affecting prospects for rural economic development, human rights and dignity, cultural survival, political stability, conservation of the environment, and efforts to combat climate change. To inform advocacy and action on community land rights, RRI has published Who Owns the World’s Land? A global baseline of formally recognized indigenous & community land rights (“the global baseline”), which identifies the amount of land national governments have formally recognized as own

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Annual Report 2022: Our Coalition’s Pivotal Successes of the Last Year. Rights and Resources Initiative, 2023. http://dx.doi.org/10.53892/xsrq4719.

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Globally, we saw significant developments in 2022, marked by a continued increase in recognition for the role played by Indigenous Peoples (IPs), local communities (LCs) and Afro-descendant Peoples (ADPs) in climate and conservation action. Donors and philanthropies formalized new funding commitments for these groups and proponents of voluntary carbon markets created “high integrity” frameworks to safeguard human rights. This report gives a snapshot of just some of our coalition’s pivotal successes that give us hope and vigor, particularly as we embark upon our new 5-year Strategic Program. Th

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From Commitments to Action: Advancing Community Rights-Based Approaches to Achieve Climate and Conservation Goals. Rights and Resources Initiative, 2023. http://dx.doi.org/10.53892/wzxz7613.

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A large proportion of the world’s remaining high-biodiversity and carbon-rich lands, forests and waters are held by Indigenous Peoples, local communities, and Afro-descendant Peoples, and a robust body of evidence demonstrates the positive environmental outcomes of their governance of these resources. Growing recognition of these roles and contributions is reflected in a range of international commitments, such as the new language on the rights of Indigenous Peoples and local communities adopted in the Kunming-Montréal Global Biodiversity Framework (GBF), commitments regarding Indigenous and c

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