Vision-based Bicyclist Detection and Tracking for Intelligent Vehicles

Hyunggi Cho, Paul Rybski, and Wende Zhang
2010 IEEE Intelligent Vehicles Symposium, June, 2010.

  • Adobe portable document format (pdf) (471KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

This paper presents a vision-based framework for intelligent vehicles to detect and track people riding bicycles in urban traffic environments. To deal with dramatic appearance changes of a bicycle according to different viewpoints as well as nonrigid nature of human appearance, a method is proposed which employs complementary detection and tracking algorithms. In the detection phase, we use multiple view-based detectors: frontal, rear, and right/left side view. For each view detector, a linear Support Vector Machine (SVM) is used for object classification in combination with Histograms of Oriented Gradients (HOG) which is one of the most discriminative features. Furthermore, a real-time enhancement for the detection process is implemented using the Integral Histogram method and a coarse-to-fine cascade approach. Tracking phase is performed by a multiple patch-based Lucas-Kanade tracker. We first run the Harris corner detector over the bounding box which is the result of our detector. Each of the corner points can be a good feature to track and, in consequence, becomes a template of each instance of multiple Lucas-Kanade trackers. To manage the set of patches efficiently, a novel method based on spectral clustering algorithm is proposed. Quantitative experiments have been conducted to show the effectiveness of each component of the proposed framework.

Sponsor: General Motors
Associated Project(s): Context-sensitive bicycle and pedestrian detection and tracking
Number of pages: 8

Text Reference
Hyunggi Cho, Paul Rybski, and Wende Zhang, "Vision-based Bicyclist Detection and Tracking for Intelligent Vehicles," 2010 IEEE Intelligent Vehicles Symposium, June, 2010.

BibTeX Reference
   author = "Hyunggi Cho and Paul Rybski and Wende Zhang",
   title = "Vision-based Bicyclist Detection and Tracking for Intelligent Vehicles",
   booktitle = "2010 IEEE Intelligent Vehicles Symposium",
   month = "June",
   year = "2010",