PETS 2017: Dataset and Challenge

Patino, Luis; Cane, Tom; Vallée, Annie; Ferryman, James

doi:10.1109/cvprw.2017.264

Cited by 31 publications

(10 citation statements)

References 10 publications

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“…Some of the datasets were constructed with surveillance as their main purpose. The PETS 2017 dataset [83] contains data from on-board surveillance systems intended to protect critical assets. PETS stands for performance evaluation of tracking and surveillance, and its application is intended to evaluate the performance and detection of various surveillance events.…”

Section: Surveillancementioning

confidence: 99%

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

et al. 2021

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The aim of this paper is to review existing publicly available and open artificial intelligence (AI) oriented datasets in different domains and subdomains of the railway sector. The contribution of this paper is an overview of AI-oriented railway data published under Creative Commons (CC) or any other copyright type that entails public availability and freedom of use. These data are of great value for open research and publications related to the application of AI in the railway sector. This paper includes insights on the public railway data: we distinguish different subdomains, including maintenance and inspection, traffic planning and management, safety and security and type of data including numerical, string, image and other. The datasets reviewed cover the last three decades, from January 1990 to January 2021. The study revealed that the number of open datasets is very small in comparison with the available literature related to AI applications in the railway industry. Another shortcoming is the lack of documentation and metadata on public datasets, including information related to missing data, collection schemes and other limitations. This study also presents quantitative data, such as the number of available open datasets divided by railway application, type of data and year of publication. This review also reveals that there are openly available APIs—maintained by government organizations and train operating companies (TOCs)—that can be of great use for data harvesting and can facilitate the creation of large public datasets. These data are usually well-curated real-time data that can greatly contribute to the accuracy of AI models. Furthermore, we conclude that the extension of AI applications in the railway sector merits a centralized hub for publicly available datasets and open APIs.

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Section: Surveillancementioning

confidence: 99%

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

et al. 2021

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“…As a preliminary handling or first-order approximation in the current calculation, a simple but typical surveillance scenario of which interaction between targets is ignored. The second portion of risk entropy, S 2 , calculated only by Equation (8) or (9). Under a constraint of time and space, probability of security events of all moving targets, i.e., P M is regarded as a constant, so the risk entropy described by Equation ( 9) is:…”

Section: ) Calculation Of Smentioning

confidence: 99%

“…The state of art CV algorithm can integrate multiple views image and multiple types of information for target detection and identity recognition, behavior understanding, and many other tasks [6]- [8]. Evaluation of CV algorithms is fruitful, and many academic conferences with significant impact have launched several regular competitions on image/video analysis such as PETS [9], TRECVID [10], PascalVOC [11] and ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [12]. These research works have shown how the image detail affects the performance of the CV algorithm.…”

Section: Introductionmentioning

confidence: 99%

Risk Entropy Modeling of Surveillance Camera for Public Security Application

Zhang

et al. 2020

IEEE Access

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Surveillance cameras are widely installed at public places around the world, and the video surveillance system plays an un-substitutable role in police work, especially in case investigation. The problem regarding the effectiveness and rationality of the video surveillance system comes into being in terms of its high demand for investment and rising public concern of over-construction potentially. To answer the question, it ought to establish mode and metrics for measuring effectiveness in theory. This article argued that the police video surveillance system is preferably a sensor network than a Physical Protect System (PPS) because its main feature is to provide the police officers with the visual information they need. Once the police cannot receive sufficient information from the system, decisions of public security are given based on limited or misleading information, and there may be some potential risks remained. Such risks of public security are not directly relevant to the integrity and value of the assets but the uncertainty of decisionmaking, which is different from the one of traditional PPS. In this paper, we proposed an entropy model for measuring the uncertainty based on attributions of video surveillance for law enforcement. Public security risk was divided into three types within the model according to the source of the risk, such as fixed targets (or restricted areas), moving objects, and video information quality. We verified the validity of the model by the simulation experiment of camera field optimization and discussed further work.

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“…There have been several challenges in the area of activity recognition including [19,26,10,23]. The focus is on classification or recognition in short untrimmed video segments.…”

Section: Activity Detectionmentioning

confidence: 99%

Continuous, Full-scope, Spatio-temporal Tracking Metric based on KL-divergence

Adams¹

2019

2019 IEEE Winter Applications of Computer Vision Workshops (WACVW)

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A unified metric is given for the evaluation of object tracking systems. The metric is inspired by KL-divergence or relative entropy, which is commonly used to evaluate clustering techniques. Since tracking problems are fundamentally different from clustering, the components of KL-divergence are recast to handle various types of tracking errors (i.e., false alarms, missed detections, merges, splits). Scoring results are given on a standard tracking dataset (Oxford Town Centre Dataset), as well as several simulated scenarios. Also, this new metric is compared with several other metrics including the commonly used Multiple Object Tracking Accuracy metric. In the final section, advantages of this metric are given including the fact that it is continuous, parameter-less and comprehensive.

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PETS 2017: Dataset and Challenge

Cited by 31 publications

References 10 publications

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

Risk Entropy Modeling of Surveillance Camera for Public Security Application

Continuous, Full-scope, Spatio-temporal Tracking Metric based on KL-divergence

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