Title: Temporally Identity-Aware SSD with Attentional LSTM,
Abstract: Temporal object detection has attracted significant attention, but most
popular detection methods can not leverage the rich temporal information in
videos. Very recently, many different algorithms have been developed for video
detection task, but 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 signal-shot detector (TSSD) for
real-world detection. Distinct from previous methods, we take aim at temporally
integrating pyramidal feature hierarchy using ConvLSTM, and design a novel
structure including a low-level temporal unit as well as a high-level one
(HL-TU) for multi-scale feature maps. Moreover, we develop a creative temporal
analysis unit, namely, attentional ConvLSTM (AC-LSTM), in which a temporal
attention module is specially tailored for background suppression and scale
suppression while a ConvLSTM integrates attention-aware features through time.
An association loss is designed for temporal coherence. Besides, online tubelet
analysis (OTA) is exploited for identification. Finally, our method is
evaluated on ImageNet VID dataset and 2DMOT15 dataset. Extensive comparisons on
the detection and tracking capability validate the superiority of the proposed
approach. Consequently, the developed TSSD-OTA is fairly faster and achieves an
overall competitive performance in terms of detection and tracking. The source
code will be made available.