Work

Automatic multi-vehicle tracking using video cameras: An improved CAMShift approach

Year: 2013

Type: article

Abstract: Video cameras play an important role in achieving the potentials promised by Intelligent Transportation Systems. In particular, video tracking systems have been widely applied in traffic flow data collection and incident detection by tracking and analyzing vehicle trajectories through video image processing. Focusing on current vehicle tracking methods that are limited to complex traffic scenes such as target non-uniqueness and color distraction in real traffic situations, this paper proposes an improved Continuously Adaptive Mean Shift (CAMShift) method for automatic multi-vehicle tracking using video cameras. The proposed method firstly uses a background subtraction method for automatically detecting, selecting, and initializing targets, i.e., vehicles. Moreover, a motion estimation method is proposed for estimating vehicle motion states and predicting the new locations of the vehicles. Finally, both the color and edge feature distributions of the vehicles are extracted and a mixed model is established for searching the matching vehicles in the next image frame. The proposed method was comparatively evaluated with the traditional CAMShift method using the same video sequences captured from real traffic situations. Evaluation results showed that the proposed method is efficient for accurately tracking vehicle movements, and is promising for practical applications.

Source: KSCE Journal of Civil Engineering

DOI: 10.1007/s12205-013-0263-7

Authors: Jingxin Xia, Wenming Rao, Wei Huang, Zhenbo Lu

Institution: Southeast University

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $1,800