Vehicle Detection under Various Lighting Conditions by Incorporating Particle Filter
- 1 September 2007
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We propose an automatic system to detect preceding vehicles on the highway under various lighting and different weather conditions based on the computer vision technologies. To adapt to different characteristics of vehicle appearance at daytime and nighttime, four cues including underneath, vertical edge, symmetry and taillight are fused for the preceding vehicle detection. By using particle filter with four cues through the processes including initial sampling, propagation, observation and cue fusion and evaluation, particle filter accurately generates the vehicle distribution. Thus, the proposed system can successfully detect and track preceding vehicles and be robust to different lighting conditions. Unlike normal particle filter focuses on a single target distribution in a discrete state space, we detect multiple vehicles with particle filter through a high-level tracking strategy using clustering technique called basic sequential algorithmic scheme (BSAS). Finally, experimental results for several videos from different scenes are provided to demonstrate the effectiveness of our proposed system.Keywords
This publication has 13 references indexed in Scilit:
- On-road vehicle detection: a reviewIEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
- Multiple Object Tracking with Kernel Particle FilterPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Online multitarget detection and tracking using sequential Monte Carlo methodsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Stochastic Car Tracking With Line- and Color-Based FeaturesIEEE Transactions on Intelligent Transportation Systems, 2004
- Towards robust multi-cue integration for visual trackingMachine Vision and Applications, 2003
- Sequential Monte Carlo methods for multiple target tracking and data fusionIEEE Transactions on Signal Processing, 2002
- Visual perception of obstacles and vehicles for platooningIEEE Transactions on Intelligent Transportation Systems, 2000
- CONDENSATION—Conditional Density Propagation for Visual TrackingInternational Journal of Computer Vision, 1998
- Intensity and Edge-Based Symmetry Detection with an Application to Car-FollowingComputer Vision and Image Understanding, 1993
- A New Approach to Linear Filtering and Prediction ProblemsJournal of Basic Engineering, 1960