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 real-time online object detection in videos. In this paper, based on the attention mechanism and convolutional long short-term memory (ConvLSTM), we propose a temporal single-shot detector (TSSD) for real-world detection. Distinct from the 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 for multiscale feature maps. Moreover, we develop a creative temporal analysis unit, namely, attentional ConvLSTM, 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 multistep 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.

中文翻译：

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具有注意力 LSTM 的临时身份感知 SSD


时间对象检测引起了人们的广泛关注，但大多数流行的检测方法无法利用视频中丰富的时间信息。最近，已经开发了许多用于视频检测任务的算法，但很少有方法可以实现视频中的实时在线目标检测。在本文中，基于注意力机制和卷积长短期记忆（ConvLSTM），我们提出了一种用于现实世界检测的时间单次检测器（TSSD）。与之前的方法不同，我们的目标是使用 ConvLSTM 在时间上集成金字塔特征层次结构，并设计一种新颖的结构，包括低级时间单元和用于多尺度特征图的高级时间单元。此外，我们开发了一种创造性的时间分析单元，即注意力ConvLSTM，其中时间注意力机制专门用于背景抑制和尺度抑制，而ConvLSTM则集成了跨时间的注意力感知特征。关联损失和多步训练是为了时间一致性而设计的。此外，还利用在线管管分析（OTA）进行识别。我们的框架在 ImageNet VID 数据集和 2DMOT15 数据集上进行评估。对检测和跟踪能力的广泛比较验证了所提出方法的优越性。因此，所开发的TSSD-OTA在检测和跟踪方面实现了较快的速度和整体竞争性能。最后，对水下物体抓取进行了现实世界的机动。