Abstract
Object detection is a significant step of intelligent video surveillance. The existing methods achieve the goals by technically designing or learning special features and detection models. Conversely, we propose a method to simulate the mechanism of memory and prediction in our brain. Firstly, a fix-sized window is slid on a static image to generate sequences. Then, a convolutional neural network extracts the sequence features. Finally, a long short-term memory receives these sequence features in proper order to memorize and recognize the sequential patterns. Our contributions are 1) a memory-based classification model in which both of feature learning and sequence learning are integrated subtly, and 2) a memory-based prediction model which is specially designed to predict the potential object locations in the surveillance scene. Compared with the state-of-the-art methods, our method obtains the best performance on three surveillance datasets. Our method may give some new insights on object detection researches.