TSSD: Temporal Single-Shot Detector Based on Attention and LSTM

摘要

Temporal object detection has attracted significant attention, but most popular methods can not leverage the rich temporal information in video or robotic vision. Although many different algorithms have been developed for video detection task, real-time online approaches are frequently deficient. In this paper, based on attention mechanism and convolutional long short-term memory (ConvLSTM), we propose a temporal single-shot detector (TSSD)for robotic vision. Distinct from previous methods, we aim to temporally integrate pyramidal feature hierarchy using ConvLSTM, and design a novel structure including a high-level ConvLSTM unit as well as a low-level one (HL-LSTM)for multi-scale feature maps. Moreover, we develop a creative temporal analysis unit, namely, ConvLSTM-based attention and attention-based ConvLSTM (A&CL), in which the ConvLSTM-based attention is specially tailored for background suppression and scale suppression while the attention-based ConvLSTM temporally integrates attention-aware features. Finally, our method is evaluated on ImageNet VID dataset. Extensive comparisons on detection performance confirm the superiority of the proposed approach, and the developed TSSD achieves a considerably enhanced accuracy vs. speed trade-off, i.e., 64.8% mAP vs. 27 FPS.