Journal ArticleDOI
Traffic monitoring and accident detection at intersections
TLDR
An algorithm, referred to as spatio-temporal Markov random field, for traffic images at intersections, that models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the x-y image axes as well as the time axes.Abstract:
We have developed an algorithm, referred to as spatio-temporal Markov random field, for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the x-y image axes as well as the time axes. Our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%-96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness.read more
Citations
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Proceedings ArticleDOI
Real-World Anomaly Detection in Surveillance Videos
TL;DR: The experimental results show that the MIL method for anomaly detection achieves significant improvement on anomaly detection performance as compared to the state-of-the-art approaches, and the results of several recent deep learning baselines on anomalous activity recognition are provided.
Proceedings ArticleDOI
ShiDianNao: shifting vision processing closer to the sensor
Zidong Du,Robert Fasthuber,Tianshi Chen,Paolo Ienne,Ling Li,Luo Tao,Xiaobing Feng,Yunji Chen,Olivier Temam +8 more
TL;DR: This paper proposes an accelerator which is 60x more energy efficient than the previous state-of-the-art neural network accelerator, designed down to the layout at 65 nm, with a modest footprint and consuming only 320 mW, but still about 30x faster than high-end GPUs.
Journal ArticleDOI
A Review of Computer Vision Techniques for the Analysis of Urban Traffic
TL;DR: A comprehensive review of the state-of-the-art computer vision for traffic video with a critical analysis and an outlook to future research directions is presented.
Journal ArticleDOI
A system for learning statistical motion patterns
TL;DR: Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.
Journal ArticleDOI
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
Brendan Morris,Mohan M. Trivedi +1 more
TL;DR: The methods reviewed are intended for real-time surveillance through definition of a diverse set of events for further analysis triggering, including virtual fencing, speed profiling, behavior classification, anomaly detection, and object interaction.
References
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Proceedings ArticleDOI
Adaptive background mixture models for real-time tracking
Chris Stauffer,W.E.L. Grimson +1 more
TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.