Robust object detection based on deformable part model and improved scale invariant feature transform

We propose an approach to improve the detection results of a generic offline trained detector on frames from a specific video. For two consecutive frames of a video with the object, deformable part model (DPM) detection is performed to get the original detections. Then the image patches corresponding to the detected root box and part boxes were respectively obtained. Thirdly, improved scale invariant feature transform features (SIFT) from those image patches were extracted and matched with the SIFT features by KD-Tree. K-means clustering the angle and scale of matched keypoints to filter out the uncorrected matches and further remove false matches by RANSAC algorithm. Finally, the SIFT_DPM detection result from the matches between image patches of continuous frames was obtained. We focus on methods with high precision detection results since it is necessitated in real application. Extensive experiments with state-of-the-art detector demonstrate the efficacy of our approach.