RoadTrack: Tracking Road Agents in Dense and Heterogeneous Environments.
Year: 2019
Type: preprint
Abstract: We present an algorithm to track traffic agents in dense videos. Our approach is designed for heterogeneous traffic scenarios that consist of different agents such as pedestrians, two-wheelers, cars, buses etc. sharing the road. We present a novel Heterogeneous Traffic Motion and Interaction model (HTMI) to predict the motion of agents by modeling collision avoidance and interactions between the agents. We implement HTMI within the tracking-by-detection paradigm and use background subtracted representations of traffic agents to extract binary tensors for accurate tracking. We highlight the performance on a dense traffic videos and observe an accuracy of 75.8%. We observe up to 4 times speedup over prior tracking algorithms on standard traffic datasets.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2955243910