Research On Fast Pedestrian Detection In Video

The image human target recognition has become a research hotspot of computer vision and pattern recognition, as it has played a very important role in the field of security monitoring, human-computer interaction and military surveillance. However, the accuracy and real-time of multi-target human detection are suffering a great deal of challenges attributed to non-rigid deformation caused by the movement of pedestrians, a plurality of target occlusion, illumination changes and complex background environmental conditions.This paper uses HOG feature to complete human detection system, and improve P.Felzenszwalb target detection algorithm based on multi-part and the latest cascade detection algorithm. In terms of speed, firstly, three frame differencing is used to extract the moving foreground. Secondly, the number of pyramid layer could be decreased according to the size of foreground, while using PCA to reduce the dimension of the HOG feature. Finally, the processing speed can be improved significantly by the use of the fast Fourier transform in the model process. These methods greatly speed detection, which achieves the capacity of quasi-real-time detection (i.e.100ms/frame) under single-threaded. Toward better adapt to the practice monitoring system, the paper also takes into account the target detection under various illumination and environmental interference, such as strong light, weak light dusk, background, grove swing, shadow and so on. For these target detection scene, the image histogram equalization and the weighted scoring system are used to reduce the undetected rate and false detection rate. The paper also uses C language to automatically and manually collect human target data set of the actual scene, to facilitate the training of specific target detection template and improve detection accuracy in a variety of environments. Finally, we make the necessary analysis of accurate detection rate and false detection rate in the actual scene. The test results show that, compared with the original, the optimized human detection algorithm based on multi-part human detection, greatly improved in the speed and accuracy of performance. This paper completes the implementation and optimization of the algorithm that can be applied to multi-target tracking field, meet the requirements of the test robustness and speed, and also reaches further conditions for the mobile phone platform.