On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling dynamic substate chains among massive states
Intelligent Data Analysis - Knowledge Discovery from Data Streams
Online boosting for vehicle detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Approximate vehicle waiting time estimation using adaptive video-based vehicle tracking
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Road Traffic Parameters Estimation by Dynamic Scene Analysis: A Systematic Review
International Journal of Grid and High Performance Computing
| Hi-index | 0.00 |
This paper presents an in-vehicle real-timemonocular precrash vehicle detection system.The systemacquires grey level images through a forward facing lowlight camera and achieves an average detection rate of 10Hz.The vehicle detection algorithm consists of two main steps:multi-scale driven hypothesis generation and appearance-based hypothesis verification. In the multi-scale hypothesis generation step, possible image locations where vehiclesmight be present are hypothesized. This step uses multi-scale techniques to speed up detection but also to improvesystem robustness by making system performance less sensitive to the choice of certain parameters. Appearance-basedhypothesis verification verifies those hypothesis using HaarWavelet decomposition for feature extraction and SupportVector Machines (SVMs) for classification. The monocular system was tested under different traffic scenarios (e.g.,simply structured highway, complex urban street, varyingweather conditions), illustrating good performance.