Journal
PATTERN RECOGNITION
Volume 67, Issue -, Pages 73-84Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2017.01.030
Keywords
Convolutional neural network; Long short-term memory; Object detection
Funding
- National Natural Science Foundation of China [61375038]
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Object detection is a significant step of intelligent surveillance. The existing methods achieve the goals by technically designing or learning special features and detection models. Conversely, we propose an effective method for accurate object detection, which is inspired by the mechanism of memory and prediction in our brain. Firstly, a fix-sized window is slid on a static image to generate an image sequence. Then, a convolutional neural network extracts a feature sequence from the image sequence. Finally, a long short-term memory receives these sequential 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 potential object locations in the surveillance scenes. Compared with some state-of-the-art methods, our method obtains the best performance in term of accuracy on three surveillance datasets. Our method may give some new insights on object detection researches. (C) 2017 Elsevier Ltd. All rights reserved.
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