STARNet: spatio-temporal aware recurrent network for efficient video object detection on embedded devices

The challenge of converting various object detection methods from image to video remains unsolved. When applied to video, image methods frequently fail to generalize effectively due to issues, such as blurriness, different and unclear positions, low quality, and other relevant issues. Additionally, the lack of a good long-term memory in video object detection presents an additional challenge. In the majority of instances, the outputs of successive frames are known to be quite similar; therefore, this fact is relied upon. Furthermore, the information contained in a series of successive or non-successive frames is greater than that contained in a single frame. In this study, we present a novel recurrent cell for feature propagation and identify the optimal location of layers to increase the memory interval. As a result, we achieved higher accuracy compared to other proposed methods in other studies. Hardware limitations can exacerbate this challenge. The paper aims to implement and increase the efficiency of the methods on embedded devices. We achieved 68.7% mAP accuracy on the ImageNet VID dataset for embedded devices in real-time and at a speed of 52 fps.