Local track to detect for video object detection
by Biao Zeng; Shan Zhong; Lifan Zhou; Zhaohui Wang; Shengrong Gong
International Journal of Computer Applications in Technology (IJCAT), Vol. 67, No. 2/3, 2021

Abstract: The existing methods for video object detection are generally achieved from searching the objects through the entire image. However, they always suffer from large computation consumption as a result of dozens of similar images needing to be operated. To relieve this problem, we propose a Local Track to Detect (LTD) framework to detect video objects by predicting the movements of objects in local areas. LTD can automatically determine key frames and non-key frames, the objects in key frames can be detected by the single frame detector, and the objects in non-key frames can be efficiently detected by the movement prediction module. LTD also has a Siamese module to predict whether objects between the key frame and the non-key frame are the same object to ensure the accuracy of the movement prediction module. Compared with other previous work, our method is more efficient and achieves state-of-the-art performance.

Online publication date: Thu, 17-Mar-2022

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