A motion-based approach to detect persons in low-resolution video

The paper proposes a motion-based technique to detect persons in a low-resolution video, where the persons look like tiny blobs. The tiny blob-like appearance of the persons are due to camera position which is at a distance from the person(s). The proposed technique uses integral matrix based different spatial and temporal features. Gradient weighted optical flow (GWOF) is calculated for each frame of the video clip to minimize background noise. Spatial filters are used to extract motion features from the GWOF based integral matrices. The combination of image gradient and GWOF features extracts static and moving persons present in the video. The AdaBoost learning technique is used for training. The training is performed using features derived from the positive samples of bounding boxes in a video frame containing a person and negative samples with bounding boxes without a person. The proposed technique is applied on benchmark Tower dataset, UT-Interaction dataset and PETS 2007 dataset. We have obtained approximately 2 to 10 % improvement in the performance compared to the states-of-the-art.