  Temporal object detection has attracted significant attention, but most
popular detection methods cannot leverage rich temporal information in videos.
Very recently, many algorithms have been developed for video detection task,
yet very few approaches can achieve \emph{real-time online} object detection in
videos. In this paper, based on attention mechanism and convolutional long
short-term memory (ConvLSTM), we propose a temporal single-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
(LH-TU) for multi-scale feature maps. Moreover, we develop a creative temporal
analysis unit, namely, attentional ConvLSTM (AC-LSTM), in which a temporal
attention mechanism is specially tailored for background suppression and scale
suppression while a ConvLSTM integrates attention-aware features across time.
An association loss and a multi-step training are designed for temporal
coherence. Besides, an online tubelet analysis (OTA) is exploited for
identification. Our framework 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 achieves a fast speed and an overall competitive performance in terms
of detection and tracking. Finally, a real-world maneuver is conducted for
underwater object grasping. The source code is publicly available at
https://github.com/SeanChenxy/TSSD-OTA.
