Vision-based Bicyclist Detection and Tracking for Intelligent Vehicles - Robotics Institute Carnegie Mellon University

Vision-based Bicyclist Detection and Tracking for Intelligent Vehicles

Hyunggi Cho, Paul Rybski, and Wende Zhang
Tech. Report, CMU-RI-TR-10-11, Robotics Institute, Carnegie Mellon University, 2010

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.

BibTeX

@techreport{Cho-2010-10389,
author = {Hyunggi Cho and Paul Rybski and Wende Zhang},
title = {Vision-based Bicyclist Detection and Tracking for Intelligent Vehicles},
year = {2010},
month = {January},
institute = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-10-11},
}