Memory-based pedestrian detection through sequence learning

Human recognize an object through eyes scanning in a certain order. We think that the proper order is helpful for capturing useful characteristics, which makes our recognition process rapidly and accurately. Therefore, we propose a memory-based sequence learning model to simulate the human recognition process. Firstly, we divide the image without overlapping to generate the sequence. Then, a convolutional neural network is used for feature extraction. Next, the sequence is re-sorted by order of importance. Finally, a long short-term memory successively receives the sequence to memorize the sequential patterns and predict the sequence label. In addition, we propose a joint learning method to make our model efficiently learn both of the sequence order and the sequence patterns. Our model is applied in the region-based detection framework for pedestrian detection. Compared with the state-of-the-art methods on two pedestrian datasets, our method achieves the comparable performance in term of accuracy and speed.