HOG Classifier Research Articles

A novel background updation algorithm using fuzzy c-means clustering for pedestrian detection

The task of pedestrian detection in video surveillance applications will face challenges like dynamic background changes, false human detection (shadow), and illumination variations. In literature, many approaches have been proposed to resolve these challenges. But their performance is not up to the mark. Thus this paper proposes efficient pedestrian detection including shadow removal and automatic dynamic background update. For this firstly, a background frame is initialized where no moving object is present. Then a background subtraction algorithm is applied to each of the key frames from the live video to detect the foreground objects (using fuzzy C means clustering followed by mean absolute difference). Later on this segmented foreground a contour is estimated and passed through the HOG classifier for pedestrian detection. The performance of the proposed approach has been compared using various datasets & state-of-the-art approaches and found to the best with an average precision of 98 %, unlike the others.

Using CNN and HOG Classifier to Improve Facial Expression Recognition

Using CNN and HOG Classifier to Improve Facial Expression Recognition

Human Detection and Depth Calculation Using Stereographic Vision with Pixel Disparity and Hog Classifier

Human Detection and Depth Calculation Using Stereographic Vision with Pixel Disparity and Hog Classifier

Research and Implementation of Real-Time Pedestrian Detection Algorithm

Pedestrian detection has a broad application prospect in automotive assisting driving system, but the real time performance is very poor in most common used detection methods. This paper presents a fast algorithm to realize the real-time pedestrian detection. The Local Binary Patterns (LBP) is used to describe the local texture information with the feature of less calculation, the HOG classifier to extract a typical feature of pedestrian’s edge, and then SVM to train and classify on the databases of INRIA and MIT. While scanning the images, interest regions are extracted to speed up the detection. Series of experiment results shows that the proposed pedestrian detecting strategy is effective and efficient.

Pedestrian Tracking Across Panning Camera Network

Video surveillance technology is getting important today, in order to maintain the safety of pedestrians passing through public spaces. Tracking pedestrians across the camera network is important to understand each pedestrian’s behavior from the image sequence of a long period. For that purpose, we developed an occlusion robust tracking algorithm of pedestrians in the panning images by the combination between the S-T MRF model and pattern recognition methods of Snakes and HOG classifier. Tracking in panning images would extend the field of view of single camera. In addition, we developed an algorithm to match pedestrians between cameras which have overlapping area with each other in their field of view. Finally, the tracking algorithm in panning images and the pedestrian matching algorithm between the overlapping images were combined to extend the area of pedestrian surveillance.