Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Associated evolution of a support vector machine-based classifier for pedestrian detection
Information Sciences: an International Journal
Real-time pedestrian detection and tracking at nighttime for driver-assistance systems
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online core vector machine with adaptive MEB adjustment
Pattern Recognition
Research collaboration and ITS topic evolution: 10 years at T-ITS
IEEE Transactions on Intelligent Transportation Systems
Sparse regularization for semi-supervised classification
Pattern Recognition
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
TED: A texture-edge descriptor for pedestrian detection in video sequences
Pattern Recognition
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
Rapid pedestrian detection in unseen scenes
Neurocomputing
Transfer learning for pedestrian detection
Neurocomputing
Designing an integrated driver assistance system using image sensors
Journal of Intelligent Manufacturing
A self-constructing cascade classifier with AdaBoost and SVM for pedestriandetection
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
The ultimate purpose of a pedestrian-detection system (PDS) is to reduce pedestrian-vehicle-related injury. Most such systems tend to adopt expensive sensors, such as infrared devices, in expectation of better performance. In comparison, a low-cost optical-camera-based system has much potential practical value, including a greater detection range, and can easily be trained to detect other objects. However, such low-cost systems are difficult to design (e.g., little original information can be collected, and the scene is very complex). To address these problems, an effective and reliable classifier is needed. The classifier should have a proper structure, its features need to be well selected, and a large number of high-quality samples are necessary for training. In this paper, we present a low-cost PDS which only uses a single optical camera. We design a cascade classifier to achieve an effective and reliable detection. First, our system scans two sequential frames at each zoom scale with a sliding window. Second, with each window, both appearance and motion features are extracted. A well-trained cascade classifier, combining statistical learning with a decomposed support-vector-machine classifier, then determines whether the window contains a human body. At the same time, to provide as much information as possible about the pedestrian, a small-scale weighted template tree trained by a coevolutionary algorithm is adopted to identify each pedestrian's direction, and the distance of each from the vehicle is also provided using an estimation algorithm. During the training procedure, we select key features by using the AdaBoost algorithm and a large number of high-quality samples. Experimental results demonstrate that the system is suitable for pedestrian detection in city traffic: The detection speed is more than 10 ft/s, the detection rate reaches 80%, and the false positive rate is no more than 0.30/00.