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Academic literature on the topic 'Chokepoint dataset'
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Journal articles on the topic "Chokepoint dataset"
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Koo, Ja Hyung, Se Woon Cho, Na Rae Baek, and Kang Ryoung Park. "Face and Body-Based Human Recognition by GAN-Based Blur Restoration." Sensors 20, no. 18 (2020): 5229. http://dx.doi.org/10.3390/s20185229.
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The long-distance recognition methods in indoor environments are commonly divided into two categories, namely face recognition and face and body recognition. Cameras are typically installed on ceilings for face recognition. Hence, it is difficult to obtain a front image of an individual. Therefore, in many studies, the face and body information of an individual are combined. However, the distance between the camera and an individual is closer in indoor environments than that in outdoor environments. Therefore, face information is distorted due to motion blur. Several studies have examined deblurring of face images. However, there is a paucity of studies on deblurring of body images. To tackle the blur problem, a recognition method is proposed wherein the blur of body and face images is restored using a generative adversarial network (GAN), and the features of face and body obtained using a deep convolutional neural network (CNN) are used to fuse the matching score. The database developed by us, Dongguk face and body dataset version 2 (DFB-DB2) and ChokePoint dataset, which is an open dataset, were used in this study. The equal error rate (EER) of human recognition in DFB-DB2 and ChokePoint dataset was 7.694% and 5.069%, respectively. The proposed method exhibited better results than the state-of-art methods.
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Aulicino, Giuseppe, Antonino Ian Ferola, Laura Fortunato, et al. "Expendable bathythermograph (XBT) data collected along the Southern Ocean chokepoint between Aotearoa / New Zealand and Antarctica, 1994–2024." Earth System Science Data 17, no. 6 (2025): 2625–40. https://doi.org/10.5194/essd-17-2625-2025.
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Abstract. This study presents the water column temperature data collected during several cruises on board the Italica, Araon, and Laura Bassi research vessels in the framework of the Climatic Long-term Interaction for the Mass balance in Antarctica (CLIMA), Southern Ocean Chokepoints Italian Contribution (SOChIC), and Marine Observatory of the Ross Sea (MORSea) projects funded by the Italian National Antarctic Research Program (PNRA). Data were collected between Aotearoa / New Zealand and the Ross Sea during the austral summers from 1994/1995 to 2023/2024. Across this chokepoint of the Antarctic Circumpolar Current, expendable bathythermograph (XBT) Sippican T7 probes were launched with a regular 20 km sampling, providing temperature profiles with a vertical resolution of 65 cm and a maximum nominal depth of 760 m. All temperature profiles underwent rigorous quality control, including a general malfunctioning verification, the removal of spikes, the consistency check of adjacent profiles, the comparison to regional oceanographic features and satellite altimetry observations, and a final visual check by the operator. Data quality checks led us to discard about 12 % of acquired XBT measurements. The full XBT dataset can be accessed as text format files via the following link: https://doi.org/10.5281/zenodo.14848849 (Aulicino et al., 2025). This dataset contributes to the improvement of our understanding of Southern Ocean features, being highly valuable for studies focusing on climate variability, especially across the Antarctic Circumpolar Current and its fronts. Furthermore, we expect that the collected XBT data will serve as a useful tool for the calibration and validation of recent satellite observations and for the improvement of Southern Ocean oceanographic simulations.
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Koo, Ja Hyung, Se Woon Cho, Na Rae Baek, and Kang Ryoung Park. "Multimodal Human Recognition in Significantly Low Illumination Environment Using Modified EnlightenGAN." Mathematics 9, no. 16 (2021): 1934. http://dx.doi.org/10.3390/math9161934.
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Human recognition in indoor environments occurs both during the day and at night. During the day, human recognition encounters performance degradation owing to a blur generated when a camera captures a person’s image. However, when images are captured at night with a camera, it is difficult to obtain perfect images of a person without light, and the input images are very noisy owing to the properties of camera sensors in low-illumination environments. Studies have been conducted in the past on face recognition in low-illumination environments; however, there is lack of research on face- and body-based human recognition in very low illumination environments. To solve these problems, this study proposes a modified enlighten generative adversarial network (modified EnlightenGAN) in which a very low illumination image is converted to a normal illumination image, and the matching scores of deep convolutional neural network (CNN) features of the face and body in the converted image are combined with a score-level fusion for recognition. The two types of databases used in this study are the Dongguk face and body database version 3 (DFB-DB3) and the ChokePoint open dataset. The results of the experiment conducted using the two databases show that the human verification accuracy (equal error rate (ERR)) and identification accuracy (rank 1 genuine acceptance rate (GAR)) of the proposed method were 7.291% and 92.67% for DFB-DB3 and 10.59% and 87.78% for the ChokePoint dataset, respectively. Accordingly, the performance of the proposed method was better than the previous methods.
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Ushasukhanya, S., and S. Jothilakshmi. "Optimization of Regional - Convolutional Neural Network for Electricity Conservation Using Arduino." Webology 18, SI01 (2021): 32–46. http://dx.doi.org/10.14704/web/v18si01/web18005.
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The demand for electrical energy in developing countries is apparently increasing thereby creating a large gap between the availability of the electrical resource and its growing demand. Globally reputed energy economists have recognized that 25% of reduction in energy consumption can be achieved by adopting efficient energy conservation techniques This paper presents one of the simplest ways of conservation techniques that enables the electric power supply only when it is actually needed. It is an automatic system that functions with the existing CCTV surveillance camera to enable/disable the electric power supply, only in the location where human is present / absent respectively. The proposed approach is demonstrated without the use of sensors, based on Regional Convolutional Neural Network (R-CNN). A new R-CNN model is constructed for CHOKEPOINT dataset and the optimization is done using Nadam technique. The results are then fed into Arduino micro controller to control the electric supply based on the presence/absence of human in the particular region.
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Napa, Lakshmi, and P. Arakeri Megha. "A novel sketch based face recognition in unconstrained video for criminal investigation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1499–509. https://doi.org/10.11591/ijece.v13i2.pp1499-1509.
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Face recognition in video surveillance helps to identify an individual by comparing facial features of given photograph or sketch with a video for criminal investigations. Generally, face sketch is used by the police when suspect’s photo is not available. Manual matching of facial sketch with suspect’s image in a long video is tedious and time-consuming task. To overcome these drawbacks, this paper proposes an accurate face recognition technique to recognize a person based on his sketch in an unconstrained video surveillance. In the proposed method, surveillance video and sketch of suspect is taken as an input. Firstly, input video is converted into frames and summarized using the proposed quality indexed three step cross search algorithm. Next, faces are detected by proposed modified Viola-Jones algorithm. Then, necessary features are selected using the proposed salp-cat optimization algorithm. Finally, these features are fused with scale-invariant feature transform (SIFT) features and Euclidean distance is computed between feature vectors of sketch and each face in a video. Face from the video having lowest Euclidean distance with query sketch is considered as suspect’s face. The proposed method’s performance is analyzed on Chokepoint dataset and the system works efficiently with 89.02% of precision, 91.25% of recall and 90.13% of F-measure.
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S., Ushasukhanya, and Jothilakshmi S. "Real-time human detection for electricity conservation using pruned-SSD and arduino." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1510. http://dx.doi.org/10.11591/ijece.v11i2.pp1510-1520.
Full textAbstract:
Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a Modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame.
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Lakshmi, Napa, and Megha P. Arakeri. "A novel sketch based face recognition in unconstrained video for criminal investigation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1499. http://dx.doi.org/10.11591/ijece.v13i2.pp1499-1509.
Full textAbstract:
Face recognition in video surveillance helps to identify an individual by comparing facial features of given photograph or sketch with a video for criminal investigations. Generally, face sketch is used by the police when suspect’s photo is not available. Manual matching of facial sketch with suspect’s image in a long video is tedious and time-consuming task. To overcome these drawbacks, this paper proposes an accurate face recognition technique to recognize a person based on his sketch in an unconstrained video surveillance. In the proposed method, surveillance video and sketch of suspect is taken as an input. Firstly, input video is converted into frames and summarized using the proposed quality indexed three step cross search algorithm. Next, faces are detected by proposed modified Viola-Jones algorithm. Then, necessary features are selected using the proposed salp-cat optimization algorithm. Finally, these features are fused with scale-invariant feature transform (SIFT) features and Euclidean distance is computed between feature vectors of sketch and each face in a video. Face from the video having lowest Euclidean distance with query sketch is considered as suspect’s face. The proposed method’s performance is analyzed on Chokepoint dataset and the system works efficiently with 89.02% of precision, 91.25% of recall and 90.13% of F-measure.
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Ushasukhanya, S., and S. Jothilakshmi. "Real-time human detection for electricity conservation using pruned-SSD and arduino." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1510–20. https://doi.org/10.11591/ijece.v11i2.pp1510-1520.
Full textAbstract:
Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame.
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Arco, E., A. Ajmar, F. Cremaschini, and C. Monaco. "SPATIO TEMPORAL DATA CUBE APPLIED TO AIS CONTAINERSHIPS TREND ANALYSIS IN THE EARLY YEARS OF THE BELT AND ROAD INITIATIVE – FROM GLOBAL TO LOCAL SCALE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2021 (June 30, 2021): 71–78. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2021-71-2021.
Full textAbstract:
Abstract. Maritime trade represents a significant part of all global import-export trade. The traffic of containerships can be monitored through Automatic Identification System (AIS), due to the fact that the International Maritime Organization (IMO) regulation requires AIS to be fitted aboard all ships of 300 gross tonnage and upwards engaged on international voyages. The approach proposed by the authors aimed to extract value added information from an AIS dataset, with a focus on maritime economy. Using an AIS dataset of global position of containerships from 01/01/2012 to 31/12/2016, the paper focuses on space-time data cube creation and analysis for a better understanding of maritime trades trends. Data cube creation has been tested at different spatio-temporal bins dimension and on different specific topics (TEU classes, alliances, chokepoints and port areas), analysing the sensitivity on trend results, and highlighting how appropriate spatio-temporal bins dimensions are important to effectively highlight relevant trends. Results of the trend analysis are discussed and validated with the main data and information found over the period 2012–2016. The aim of this paper is to demonstrate the suitability of this approach applied to AIS data and to highlight its limitations. The authors can conclude that the approach used has proved to be adequate in describing the evolution of the global import-export trade.
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Ammar, Sirine, Thierry Bouwmans, and Mahmoud Neji. "Face Identification Using Data Augmentation Based on the Combination of DCGANs and Basic Manipulations." Information 13, no. 8 (2022): 370. http://dx.doi.org/10.3390/info13080370.
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Recently, Deep Neural Networks (DNNs) have become a central subject of discussion in computer vision for a broad range of applications, including image classification and face recognition. Compared to existing conventional machine learning methods, deep learning algorithms have shown prominent performance with high accuracy and speed. However, they always require a large amount of data to achieve adequate robustness. Furthermore, additional samples are time-consuming and expensive to collect. In this paper, we propose an approach that combines generative methods and basic manipulations for image data augmentations and the FaceNet model with Support Vector Machine (SVM) for face recognition. To do so, the images were first preprocessed by a Deep Convolutional Generative Adversarial Net (DCGAN) to generate samples having realistic properties inseparable from those of the original datasets. Second, basic manipulations were applied on the images produced by DCGAN in order to increase the amount of training data. Finally, FaceNet was employed as a face recognition model. FaceNet detects faces using MTCNN, 128-D face embedding is computed to quantify each face, and an SVM was used on top of the embeddings for classification. Experiments carried out on the LFW and VGG image databases and ChokePoint video database demonstrate that the combination of basic and generative methods for augmentation boosted face recognition performance, leading to better recognition results.
Book chapters on the topic "Chokepoint dataset"
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Moung, Ervin Gubin, Jamal Ahmad Dargham, and John Khoo. "Deep Learning Architectures and Their Efficacy in Surveillance Face Recognition: A Study with the ChokePoint Dataset." In Internet of Things. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1432-2_10.
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