Abstract
This paper proposes a novel algorithm for object detection and tracking combined with attribute recognition that can be used in embedded systems. The algorithm, which is based on a single-shot multi-box detector (SSD) and kernelized correlation filter (KCF), can distinguish other objects similar to the tracking object, thereby solving the problem of confusion between similar objects. Different classification tasks in the multi-attribute recognition algorithm share the feature extraction module, which uses depthwise separable convolution and global pooling instead of standard convolution and fully connected (FC) layers, thereby improving the overall recognition accuracy and computational efficiency. Additionally, an attribute weight fine-tuning mechanism is added to improve the overall precision and ensure that different tasks are fully learned according to the degree of difficulty. Moreover, this algorithm reduces the size of the model without decreasing the accuracy, making it possible to be run on an embedded device. The results of experiments performed on OTB-100 demonstrate that a superior accuracy of 82.85% is achieved, and the precision and F1 indicator values reach 69.84% and 70.85%, respectively. The precision rate (PR) and success rate (SR) of the overall algorithm respectively reach 85.34% and 80.88%, which are higher than those achieved by the SSD algorithm. However, the size of the proposed attribute recognition algorithm is only 3.05 MB, which is about 1% of the size of other algorithms, indicating that the proposed algorithm not only improves the overall recognition accuracy, but also effectively reduces the model size.
This work is supported by the National Key Research and Development Program of China under Grant 2018AAA0102702; Natural Science Foundation of Heilongjiang Province (JJ2019LH2398); Fundamental Research Funds for the Central Universities (3072020CFT0801, 3072019CF0801 and 3072019CFM0802).
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Ji, Q., Liu, H., Hou, C., Zhang, Q., Mo, H. (2021). An Object Detection and Tracking Algorithm Combined with Semantic Information. In: Xiong, J., Wu, S., Peng, C., Tian, Y. (eds) Mobile Multimedia Communications. MobiMedia 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 394. Springer, Cham. https://doi.org/10.1007/978-3-030-89814-4_62
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