Online boosting for vehicle detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
A general active-learning framework for on-road vehicle recognition and tracking
IEEE Transactions on Intelligent Transportation Systems
Improvement of adaptive cruise control performance
EURASIP Journal on Advances in Signal Processing - Special title on vehicular ad hoc networks
WalkSafe: a pedestrian safety app for mobile phone users who walk and talk while crossing roads
Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications
Salient video cube guided nighttime vehicle braking event detection
Journal of Visual Communication and Image Representation
Visual-based spatiotemporal analysis for nighttime vehicle braking event detection
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Journal of Signal Processing Systems
Active learning for on-road vehicle detection: a comparative study
Machine Vision and Applications
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Robust and reliable vehicle detection from images acquired by a moving vehicle (i.e., on-road vehicle detection) is an important problem with applications to driver assistance systems and autonomous, self-guided vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view vehicle detection. Specifically, by treating the problem of vehicle detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular vehicle detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average detection rate of 10 Hz. Our vehicle detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where vehicles might be present are extracted. This step uses multiscale techniques not only to speed up detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance.