An Efficient Pedestrian Detector Based on Saliency and HOG Features Modeling

Most of pedestrian detection existing approaches rely on applying descriptors to the entire image or use a sliding window which resize the matching window at different scales and scan the image. However, these methods suffer from low computational efficiency and time consuming. We propose in this paper the use of saliency detection based on contourlet transform to generate a region of interest (ROI). The resulting saliency map is then used for features extraction using the HOG descriptor. Finally, the distribution of the generated features is estimated by a two-parameter Weibull model. The built feature vector is after trained using a support vector regression (SVR) classifier. Thus, the proposed approach provides two contributions. (1) By designing a saliency detection, we aim to remove noisy and busy background and focus on the area where the object exists which enhance the accuracy of the classification process. (2) By modeling the generated features, we intend to reduce the training dimension and make the system computationally efficient in real-time, or soft real-time. The results of the experimental study made on the challenging INRIA data set prove the effectiveness of the proposed approach.