Relevant bibliographies by topics / Fail-detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Fail-detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Fail-detection"

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Riedel, Friedbert, and Zeno Stössel. "A fail-safe sensor for flame detection." Sensors and Actuators A: Physical 37-38 (June 1993): 534–39. http://dx.doi.org/10.1016/0924-4247(93)80092-u.

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Futsuhara, Koichi, and Masao Mukaidono. "Fail-safe obstacle detection by ultrasoric pulsed radar." IEEJ Transactions on Industry Applications 110, no. 3 (1990): 218–26. http://dx.doi.org/10.1541/ieejias.110.218.

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Rein, Nick J., Emily A. Freund, Courtney D. Jensen, Cynthia Villalobos, and J. Mark VanNess. "Technological Advancements Fail To Elicit Improvements In CVD Detection." Medicine & Science in Sports & Exercise 52, no. 7S (July 2020): 556. http://dx.doi.org/10.1249/01.mss.0000680268.77461.2c.

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Men, Yu Zhuo, Hai Bo Yu, Hua Wang, Jin Gang Gao, and Xin Pan. "Automobile Frame Side Rail Detection System Based on Machine Vision." Advanced Materials Research 605-607 (December 2012): 1527–30. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.1527.

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A machine vision on-line detection system for automobile frame side fail mounting holes is proposed in this article to solve the backward and low-efficiency problems for detection methods of large-size automobile frame side fail mounting holes. Many images captured by CDD camera are processed and analyzed by virtue of algorithms such as image stitching, threshold segmentation, edge detection, feature extraction, etc.. The developed detection system prototype has very high detection accuracy.
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Wang, Liu Min, and Bo Mo. "Power-Fail Detection and Data Storage Design for Control System." Advanced Materials Research 383-390 (November 2011): 4121–24. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.4121.

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The purpose of power-fail protection design is to ensure the certainty and integrity of system information. The key point of design includes: the signals of power-fail detection and data treatm- ent; real-time clock circuit design which is synchronous with the system or as a mark of time; using non-volatile memory (such as FRAM) or using battery backup to maintain trade volatile memory (eg RAM) power to ensure the information integrity and non-volatile storage when the power is removed.
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Sabhnani, Maheshkumar, and Gursel Serpen. "Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set." Intelligent Data Analysis 8, no. 4 (October 14, 2004): 403–15. http://dx.doi.org/10.3233/ida-2004-8406.

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Staszewski, W. J., K. Worden, R. Wardle, and G. R. Tomlinson. "Fail-safe sensor distributions for impact detection in composite materials." Smart Materials and Structures 9, no. 3 (June 1, 2000): 298–303. http://dx.doi.org/10.1088/0964-1726/9/3/308.

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SUMMATTA, Chuthong. "The Improvement of a Fail-safe Counter for Low-speed Detection." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 7 (July 2, 2021): 25–30. http://dx.doi.org/10.15199/48.2021.07.05.

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Saadoon, Muntadher, Siti Hafizah Ab Hamid, Hazrina Sofian, Hamza Altarturi, Nur Nasuha, Zati Hakim Azizul, Asmiza Abdul Sani, and Adeleh Asemi. "Experimental Analysis in Hadoop MapReduce: A Closer Look at Fault Detection and Recovery Techniques." Sensors 21, no. 11 (May 31, 2021): 3799. http://dx.doi.org/10.3390/s21113799.

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Hadoop MapReduce reactively detects and recovers faults after they occur based on the static heartbeat detection and the re-execution from scratch techniques. However, these techniques lead to excessive response time penalties and inefficient resource consumption during detection and recovery. Existing fault-tolerance solutions intend to mitigate the limitations without considering critical conditions such as fail-slow faults, the impact of faults at various infrastructure levels and the relationship between the detection and recovery stages. This paper analyses the response time under two main conditions: fail-stop and fail-slow, when they manifest with node, service, and the task at runtime. In addition, we focus on the relationship between the time for detecting and recovering faults. The experimental analysis is conducted on a real Hadoop cluster comprising MapReduce, YARN and HDFS frameworks. Our analysis shows that the recovery of a single fault leads to an average of 67.6% response time penalty. Even though the detection and recovery times are well-turned, data locality and resource availability must also be considered to obtain the optimum tolerance time and the lowest penalties.
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Liu, Shuming, Ruonan Li, Kate Smith, and Han Che. "Why conventional detection methods fail in identifying the existence of contamination events." Water Research 93 (April 2016): 222–29. http://dx.doi.org/10.1016/j.watres.2016.02.027.

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Dissertations / Theses on the topic "Fail-detection"

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Fuggetti, Giuseppe. "Design di un sistema di fault detection per un'architettura fail-safe di attuazione delle superfici di controllo di un UAV." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7964/.

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In questo lavoro di tesi si affronta una delle problematiche che si presentano oggi nell'impiego degli APR (Aeromobili a Pilotaggio Remoto): la gestione della safety. Non si può più, in altri termini, negare che tali oggetti siano parte integrante dello spazio aereo civile. Proprio su questo tema recentemente gli enti regolatori dello spazio aereo stanno proiettando i loro sforzi al fine di stabilire una serie di regolamenti che disciplinino da una parte le modalità con cui questi oggetti si interfacciano con le altre categorie di velivoli e dall'altra i criteri di idoneità perché anche essi possano operare nello spazio aereo in maniera sicura. Si rende quindi necessario, in tal senso, dotare essi stessi di un sufficiente grado di sicurezza che permetta di evitare eventi disastrosi nel momento in cui si presenta un guasto nel sistema; è questa la definizione di un sistema fail-safe. Lo studio e lo sviluppo di questa tipologia di sistemi può aiutare il costruttore a superare la barriera oggi rappresentata dal regolamento che spesso e volentieri rappresenta l'unico ostacolo non fisico per la categoria dei velivoli unmanned tra la terra e il cielo. D'altro canto, al fine di garantire a chi opera a distanza su questi oggetti di avere, per tutta la durata della missione, la chiara percezione dello stato di funzionamento attuale del sistema e di come esso può (o potrebbe) interagire con l'ambiente che lo circonda (situational awarness), è necessario dotare il velivolo di apparecchiature che permettano di poter rilevare, all'occorrenza, il malfunzionamento: è questo il caso dei sistemi di fault detection. Questi due fondamentali aspetti sono la base fondante del presente lavoro che verte sul design di un ridotto ma preponderante sottosistema dell'UAV: il sistema di attuazione delle superfici di controllo. Esse sono, infatti, l'unico mezzo disponibile all'operatore per governare il mezzo nelle normali condizioni di funzionamento ma anche l'ultima possibilità per tentare di evitare l'evento disastroso nel caso altri sottosistemi siano chiaramente fuori dalle condizioni di normale funzionamento dell'oggetto.
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Zara, Lorena. "Développement de biocapteurs à base d'aptamères pour la détection de petites molécules Engineering Light-Up Aptamers for the Detection of RNA Hairpins through Kissing Interaction Anti-Pesticide DNA Aptamers Fail to Recognize their Targets with Asserted Micromolar Dissociation Constants A malachite green light-up aptasensor 1 for the detection of 2 theophylline." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALV033.

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De nos jours, il est urgent de créer des biocapteurs peu coûteux, efficaces et simples à utiliser, capables de quantifier et de détecter rapidement des petites molécules (masse molaire <1000 g / mol) telles que les pesticides, les toxines, les antibiotiques ou les médicaments, pour protéger, à la fois la santé humaine et l’environnement. Les aptamères sont des oligonucléotides en série ARN ou ADN présentant une affinité et une spécificité élevées pour leur cible. Ils peuvent être utilisés comme éléments de reconnaissance moléculaire dans des biocapteurs, appelés aptacapteurs. Ces derniers offrent une alternative prometteuse aux méthodes conventionnelles pour la détection des petites molécules. L'objectif de cette thèse est de concevoir de nouveaux aptacapteurs spécifiques basés sur le couplage de deux stratégies : « light-up » et « kissing-complexe ». Dans un premier temps, une détection à base d'aptamères fluorescents spécifiques de deux précurseurs de microARN, let-7b et miR206, a été développée. Suivant ce concept, un aptacapteur fluorescent à double reconnaissance capable de détecter la théophylline a été par la suite créé. Enfin, deux approches utilisant la méthode d'anisotropie de fluorescence et le déplacement d’un brin complémentaire de l’aptamère ou d’un intercalant de l’ADN ont été appréhendées pour la détection de deux pesticides, l'isocarbophos et le phorate
Nowadays, there is an urgency to detect small molecules (molecular weight < 1000 g/mol) such as pesticides, toxins, antibiotics or drug residues to protect both human and environment health. It is very important to create a cost effective and simple to use sensor system able to rapidly quantify small molecules, with high efficiency of measurements. In this context, aptamers that are RNA or DNA oligonucleotides displaying high affinity and specificity for their cognate target can be used as molecular recognition element in biosensors. These biosensors called aptasensors can provide a valid and interesting alternative to conventional methods.The aim of this thesis consists in engineering new specific aptasensors based on the coupling of two strategies: light-up and kissing-complex. Firstly, we designed a fluorescent aptamer-based sensing specific of two microRNA precursors: let-7b and miR206. In a second time, based on this construct, we create a label free fluorescent double-switch aptasensor to detect theophylline. Finally, in the third work, two aptamer-based fluorescence anisotropy approaches, using the displacement concept, are investigated to detect pesticides including isocarbophos and phorate
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Jajtner, Jan. "Návrh vestavěného systému pro řízení výukového modelu rotačního kyvadla." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-232046.

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The basic aim of this work is to improve existing model of rotational inverted pendulum by adding new mechanical features, implement the control algorithm to dsPIC microcontroller and develop related control electronics thus extending the functionality of current model while making it more compact. The work contains derivation of dynamic equations both by means of analytical methods and multi-body formalism of SimMechanics. These are used to design a state controller stabilizing the pendulum in inverse position. In addition, parameters of the system are being estimated experimentally. Swing-up controller is developed to drive the pendulum to unstable position. Various state estimators are added to controller to improve the control process while comparing their overall performance. The last point is devoted to development of superior state-automaton designed to switch between different regulating modes including fail-detection algorithms providing smooth operation of the model.
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Kurt, Mehmet Can. "Fault-tolerant Programming Models and Computing Frameworks." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437390499.

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Books on the topic "Fail-detection"

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A vector-based failure detection and isolation algorithm for a dual fail-operational redundant strapdown inertial measurement unit. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1987.

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L, Bailey Melvin, and Langley Research Center, eds. A vector-based failure detection and isolation algorithm for a dual fail-operational redundant strapdown inertial measurement unit. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1987.

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L, Bailey Melvin, and Langley Research Center, eds. A vector-based failure detection and isolation algorithm for a dual fail-operational redundant strapdown inertial measurement unit. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1987.

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Book chapters on the topic "Fail-detection"

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Pont, Jamie, Budi Arief, and Julio Hernandez-Castro. "Why Current Statistical Approaches to Ransomware Detection Fail." In Lecture Notes in Computer Science, 199–216. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62974-8_12.

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Jaramillo, Johnnatan, and Julián D. Arias-Londoño. "Fail Detection in WfM/BPM Systems from Event Log Sequences Using HMM-Type Models." In Applications of Computational Intelligence, 223–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36211-9_18.

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Jain, Shefali, Anish Mathuria, and Manik Lal Das. "Misbehavior Detection in VANET." In Security, Privacy, Trust, and Resource Management in Mobile and Wireless Communications, 134–47. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4691-9.ch007.

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Vehicular Networks (VANETs) have received increased attention from researchers in recent years. VANETs facilitate various safety measures that help in controlling traffic and saving human lives. As VANETs consist of multiple entities, effective measures for VANET safety are to be addressed as per requirement. In this chapter, the authors review some existing schemes proposed for misbehavior detection. They categorize the schemes into two parts: data centric and non-data centric misbehaving detection. In data-centric misbehaving detection, the receiver believes the information rather than the source of the information. The authors compare schemes in each category with respect to their security strengths and weaknesses. The comparative results show that most of the schemes fail to address required security attributes that are essential for VANET safety.
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"An experimental study of fail-safe sensor distributions for impact detection in composite materials." In Smart Materials and Structures, 483–92. CRC Press, 1998. http://dx.doi.org/10.1201/9781482268560-67.

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Warbhe, Nitish Devendra, Rutuja Rajendra Patil, Tarun Rajesh Shrivastava, and Nutan V. Bansode. "Face Detection With Face Mask for COVID-19 Pandemic Using Neural Networks." In Advances in Healthcare Information Systems and Administration, 210–23. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7709-7.ch012.

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The COVID-19 virus can be spread through contact and contaminated surfaces; therefore, typical biometric systems like password and fingerprint are unsafe. Face recognition solutions are safer without any need of touching any device. During the COVID-19 situation as all of the people are advised to wear masks on their faces, the existing face detection technique is not able to identify the person with face occlusion. The fraudsters and thieves take advantage of this scenario and misuse the face mask, favoring them to be able to steal and commit various crimes without being identified. Face recognition methods fail to detect or recognize the face as half of the face is masked and the features are suppressed. Face recognition requires the visibility of major facial features for face normalization, orientation correction, and recognition. Thus, the chapter focuses on the facial recognition based on the feature points surrounding the eye region rather than taking the whole face as a parameter.
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Kougioumtzis, Georgios A., Maria Sofologi, Argyro Fella, Isidora Kaliotsou, Christiana Koundourou, Eleni Bonti, and Georgia Papantoniou. "Specific Learning Disabilities - Implementation of an Intervention Program." In Building Integrated Collaborative Relationships for Inclusive Learning Settings, 161–80. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6816-3.ch007.

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Over the past three decades, the interest of teachers and parents has focused on a variety of learning difficulties that students face in school settings. A significant number of students systematically fail in school, as they lack early detection of learning difficulties or effective intervention. This case study examines an elementary school student within a more general context, taking into account essential parameters such as family, school, and social environment. Furthermore, the authors thoroughly describe his difficulties in practical terms, as well as ways to address them through the implementation of an individual intervention program that responds to the needs of the student. Finally, reference is made to significant evidence that cooperation with parents as well as with a number of institutions strengthens and enhances intervention.
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Johnson, Dominic D. P. "Hedging Bets." In Strategic Instincts, 115–44. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691137452.003.0006.

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This chapter examines the strategic advantages of the fundamental attribution error (FAE). It talks about people that tend to attribute the behavior of other actors to intentional action but behavior of their own as dictated by circumstances or “situational constraints.” It also points out how situational constraints are though to be an important reason why nations fail to cooperate, descend into arms races, escalate conflicts, and ultimately end up at war. The chapter explains the perception of FAE as a threatening behavior that is intentional, such as the buildup of armies and armaments by European states prior to 1914 that was widely considered a menace to security. It explores the adaptive features, such as its aid in the detection of threats, preparations for war, and the formation of alliances.
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Tzanis, George, Christos Berberidis, and Ioannis Vlahavas. "Biological Data Mining." In Encyclopedia of Database Technologies and Applications, 35–41. IGI Global, 2005. http://dx.doi.org/10.4018/978-1-59140-560-3.ch007.

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At the end of the 1980s, a new discipline named data mining emerged. The introduction of new technologies such as computers, satellites, new mass storage media, and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of, often noisy, data efficiently in an exploratory fashion. The scope of data mining is the knowledge extraction from large data amounts with the help of computers. It is an interdisciplinary area of research that has its roots in databases, machine learning, and statistics and has contributions from many other areas such as information retrieval, pattern recognition, visualization, parallel and distributed computing. There are many applications of data mining in the real world. Customer relationship management, fraud detection, market and industry characterization, stock management, medicine, pharmacology, and biology are some examples (Two Crows Corporation, 1999).
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Tzanis, George, Christos Berberidis, and Ioannis Vlahavas. "Biological Data Mining." In Data Warehousing and Mining, 1696–705. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-951-9.ch099.

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At the end of the 1980s, a new discipline named data mining emerged. The introduction of new technologies such as computers, satellites, new mass storage media, and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of, often noisy, data efficiently in an exploratory fashion. The scope of data mining is the knowledge extraction from large data amounts with the help of computers. It is an interdisciplinary area of research that has its roots in databases, machine learning, and statistics and has contributions from many other areas such as information retrieval, pattern recognition, visualization, parallel and distributed computing. There are many applications of data mining in the real world. Customer relationship management, fraud detection, market and industry characterization, stock management, medicine, pharmacology, and biology are some examples (Two Crows Corporation, 1999).
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Nikoletseas, Sotiris, Olivier Powell, and Jose Rolim. "Geographic Routing of Sensor Data around Voids and Obstacles." In Intelligent Techniques for Warehousing and Mining Sensor Network Data, 257–79. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-328-9.ch012.

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Geographic routing is becoming the protocol of choice for many sensor network applications. Some very efficient geographic routing algorithms exist, however they require a preliminary planarization of the communication graph. Planarization induces overhead which makes this approach not optimal when lightweight protocols are required. On the other hand, georouting algorithms which do not rely on planarization have fairly low success rates and either fail to route messages around all but the simplest obstacles or have a high topology control overhead (e.g. contour detection algorithms). This chapter describes the GRIC algorithm which was designed to overcome some of those limitations. The GRIC algorithm was proposed in (Powell & Nikoletseas, 2007a). It is the first lightweight and efficient on demand (i.e. all-to-all) geographic routing algorithm which does not require planarization, has almost 100% delivery rates (when no obstacles are added), and behaves well in the presence of large communication blocking obstacles.
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Conference papers on the topic "Fail-detection"

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Wu, Zhengwang, Yuehu Liu, Shaoyi Du, Jingjun Wu, and Maojun Yuan. "A Save-Fail mechanism for face detection validation." In 2009 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2009. http://dx.doi.org/10.1109/ivs.2009.5164327.

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Gilliam, David M., Floyd D. McDaniel, and Barney L. Doyle. "Statistical Requirements For Pass-Fail Testing Of Contraband Detection Systems." In APPLICATION OF ACCELERATORS IN RESEARCH AND INDUSTRY: Twenty-First International Conference. AIP, 2011. http://dx.doi.org/10.1063/1.3586082.

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Gunderson, K., and C. Thoraval. "162. Problems with Arsenic Detection: When NIOSH and EPA Methods Fail." In AIHce 2006. AIHA, 2006. http://dx.doi.org/10.3320/1.2758873.

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Zhang, Shibo, Rawan Alharbi, Matthew Nicholson, and Nabil Alshurafa. "When generalized eating detection machine learning models fail in the field." In UbiComp '17: The 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3123024.3124409.

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Kodali, S., C. Zhang, T. Schaeffer, S. Arva, D. Nedeau, J. Yu, Y. Ma, et al. "Conductive Thin Film Fail Detection in 20 nm and 14 nm Technologies." In ISTFA 2016. ASM International, 2016. http://dx.doi.org/10.31399/asm.cp.istfa2016p0249.

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Abstract Conductive thin film residues often referred to as puddles could be challenging fails to detect. A large extant film with no distinct boundaries would make the task more challenging for a comparison between good and bad region. Advanced node 20nm and 14nm technologies mandate use of several conductive thin films in the front end of line processes, and hence a potential for high defects during initial product development stage. Use of other electrical characterization techniques in combination with scanning electron microscopy inspection will be a very powerful tool to detect the root cause affirmatively. Cross-sectional images are necessary to understand the root cause of the fails for corrective actions. This work uses three cases of power supply shorts as a platform to demonstrate the idea, demonstrating a few situations where traditional techniques might reach its limits while the authors depend on additional characterization tools to confidently detect and confirm fails.
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Stelzer, Philipp, Andreas Strasser, Christian Steger, and Norbert Druml. "Fail-Operational Shock Detection and Correction of MEMS-based Micro-Scanning LiDAR Systems." In 2020 IEEE Sensors Applications Symposium (SAS). IEEE, 2020. http://dx.doi.org/10.1109/sas48726.2020.9220034.

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Eubank, Ryan, Ella Atkins, and Stephanie Ogura. "Fault Detection and Fail-Safe Operation with a Multiple-Redundancy Air-Data System." In AIAA Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-7855.

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Sharshembiev, Kumar, Seong-Moo Yoo, Elbasher Elmahdi, Yong-Kab Kim, and Geun-Ho Jeong. "Fail-Safe Mechanism Using Entropy Based Misbehavior Classification and Detection in Vehicular Ad Hoc Networks." In 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE, 2019. http://dx.doi.org/10.1109/ithings/greencom/cpscom/smartdata.2019.00042.

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Bidarra, Rafael, Maurice Dohmen, and Willem F. Bronsvoort. "Automatic Detection of Interactions in Feature Models." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/cie-4275.

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Abstract Current feature-based modeling systems fail to adequately maintain feature semantics. This is partly due to inappropriate specification of validity conditions in feature classes, but mainly due to a lack of effective validity maintenance mechanisms throughout the modeling process. A critical aspect in this is feature interaction management. This paper presents a new approach to the detection of feature interactions, which uses semantic and interaction constraints in feature class specification. Validity maintenance is automatically performed after each modeling operation by checking these constraints, thus being able to detect a variety of interaction types. Such interactions are then analyzed, and their causes identified and reported to the user.
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Wang, Xiao, Jun Chen, Zheng Wang, Wu Liu, Shin'ichi Satoh, Chao Liang, and Chia-Wen Lin. "When Pedestrian Detection Meets Nighttime Surveillance: A New Benchmark." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/71.

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Pedestrian detection at nighttime is a crucial and frontier problem in surveillance, but has not been well explored by the computer vision and artificial intelligence communities. Most of existing methods detect pedestrians under favorable lighting conditions (e.g. daytime) and achieve promising performances. In contrast, they often fail under unstable lighting conditions (e.g. nighttime). Night is a critical time for criminal suspects to act in the field of security. The existing nighttime pedestrian detection dataset is captured by a car camera, specially designed for autonomous driving scenarios. The dataset for nighttime surveillance scenario is still vacant. There are vast differences between autonomous driving and surveillance, including viewpoint and illumination. In this paper, we build a novel pedestrian detection dataset from the nighttime surveillance aspect: NightSurveillance1. As a benchmark dataset for pedestrian detection at nighttime, we compare the performances of state-of-the-art pedestrian detectors and the results reveal that the methods cannot solve all the challenging problems of NightSurveillance. We believe that NightSurveillance can further advance the research of pedestrian detection, especially in the field of surveillance security at nighttime.
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