Real-Time Road Sign Detection Using Fuzzy-Boosting
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A Real-Time Person Detection Method for Moving Cameras
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Dense Stereo-Based ROI Generation for Pedestrian Detection
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Real-time pedestrian detection and tracking at nighttime for driver-assistance systems
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
On exploration of classifier ensemble synergism in pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
A monocular human detection system based on EOH and oriented LBP features
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Pedestrian detection and tracking using HOG and oriented-LBP features
NPC'11 Proceedings of the 8th IFIP international conference on Network and parallel computing
Intelligent automatic overtaking system using vision for vehicle detection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Vision-based active safety system for automatic stopping
Expert Systems with Applications: An International Journal
Performance analysis of pedestrian detection at night time with different classifiers
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
A survey of techniques for human detection in static images
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Robotics and Autonomous Systems
An integrative approach to accurate vehicle logo detection
Journal of Electrical and Computer Engineering
Hi-index | 0.00 |
This paper describes a comprehensive combination of feature extraction methods for vision-based pedestrian detection in Intelligent Transportation Systems. The basic components of pedestrians are first located in the image and then combined with a support-vector-machine-based classifier. This poses the problem of pedestrian detection in real cluttered road images. Candidate pedestrians are located using a subtractive clustering attention mechanism based on stereo vision. A components-based learning approach is proposed in order to better deal with pedestrian variability, illumination conditions, partial occlusions, and rotations. Extensive comparisons have been carried out using different feature extraction methods as a key to image understanding in real traffic conditions. A database containing thousands of pedestrian samples extracted from real traffic images has been created for learning purposes at either daytime or nighttime. The results achieved to date show interesting conclusions that suggest a combination of feature extraction methods as an essential clue for enhanced detection performance