Vision-based Bicyclist Detection and Tracking for Intelligent Vehicles

Vision-based Bicyclist Detection and Tracking for Intelligent Vehicles

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by Hyunggi Cho , Paul E. Rybski , Wende Zhang

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Datum	Value	Source
TITLE	Vision-based Bicyclist Detection and Tracking for Intelligent Vehicles	SVM HeaderParse 0.2
AUTHOR NAME	Hyunggi Cho	SVM HeaderParse 0.2
AUTHOR NAME	Paul E. Rybski	SVM HeaderParse 0.2
AUTHOR NAME	Wende Zhang	SVM HeaderParse 0.2
ABSTRACT	Abstract — 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. I.	SVM HeaderParse 0.2
CITATIONS	24 found	ParsCit 1.0