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Inception single shot multi-box detector with affinity propagation clustering and their application in multi-class vehicle counting

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Abstract

Multi-class vehicle detection and counting in video-based traffic surveillance systems with real-time performance and acceptable precision are challenging. This paper proposes a modified single shot multi-box convolutional neural network named Inception-SSD (ISSD) for vehicle detection and a centroid matching algorithm for vehicle counting. An Inception-like block is introduced to replace the extra feature layers in the original SSD to deal with the multi-scale vehicle detection to enhance smaller vehicles’ detection. Non-Maximum Suppression (NMS) is replaced with Affinity Propagation Clustering (APC) to improve the detection of nearby occluded vehicles. For a 300 × 300 input image, on PASCAL VOC 2007 test data set, the proposed ISSD achieved 79.3 mean Average Precision (mAP) and ran on an NVIDIA RTX2080Ti; the network attains a speed of 52.3 frames per second. ISSD with APC generates 2.7% improvement in mAP over original SSD300 while almost retaining its time efficiency. By centroid matching algorithm, the vehicles are counted class-wise with a weighted F1 of 98.5%, which is quite superior to the other recent existing research works.

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Acknowledgements

This work was funded by Vandi Technologies PTE LTD Singapore, (Grant No. VANDI/PS01/NITT1821 dated 10-09-2018)

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tamil Nadu, India

    P. M. Harikrishnan, Anju Thomas, Varun P. Gopi & P. Palanisamy

  2. Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatchewan, Canada

    Khan A. Wahid

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  1. P. M. Harikrishnan
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  2. Anju Thomas
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  5. Khan A. Wahid
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Correspondence to Varun P. Gopi.

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Harikrishnan, P.M., Thomas, A., Gopi, V.P. et al. Inception single shot multi-box detector with affinity propagation clustering and their application in multi-class vehicle counting. Appl Intell 51, 4714–4729 (2021). https://doi.org/10.1007/s10489-020-02127-y

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