Learning Behavioural Context

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Traffic Danger Recognition With Surveillance Cameras Without Training Data

We propose a traffic danger recognition model that works with arbitrary traffic surveillance cameras to identify and predict car crashes. There are too many cameras to monitor manually. Therefore, we developed a model to predict and identify car crashes from surveillance cameras based on a 3D reconstruction of the road plane and prediction of trajectories. For normal traffic, it supports real-time proactive safety checks of speeds and distances between vehicles to provide insights about possible high-risk areas. We achieve good prediction and recognition of car crashes without using any labeled training data of crashes. Experiments on the BrnoCompSpeed dataset show that our model can accurately monitor the road, with mean errors of 1.80% for distance measurement, 2.77 km/h for speed measurement, 0.24 m for car position prediction, and 2.53 km/h for speed prediction.Comment: To be published in proceedings of Advanced Video and Signal-based Surveillance (AVSS), 2018 15th IEEE International Conference on, pp. 378-383, IEE

The 2018 NVIDIA AI City Challenge

The NVIDIA AI City Challenge has been created to accelerate intelligent video analysis that helps make cities smarter and safer. With millions of traffic video cameras acting as sensors around the world, there is a significant opportunity for real-time and batch analysis of these videos to provide actionable insights. These insights will benefit a wide variety of agencies, from traffic control to public safety. The second edition of the NVIDIA AI City Challenge, being organized as a CVPR workshop, provided a forum to more than 70 academic and industrial research teams to compete and solve real-world problems using traffic camera video data. The Challenge was launched with three tracks — speed estimation, anomaly detection, and vehicle re-identification. Each track was chosen in consultation with traffic and public safety officials based on the value of potential solutions. With the largest available dataset for such tasks, and ground truth for each track, the Challenge enabled 22 teams to evaluate their solutions. Given how complex these tasks are, the results are encouraging and reflect increased value addition year over year for the Challenge

Video trajectory analysis using unsupervised clustering and multi-criteria ranking

Surveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead