#6388. Optimized MobileNet + SSD: a real-time pedestrian detection on a low-end edge device

October 2026publication date
Proposal available till 22-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Library and Information Sciences;
Media Technology;
Information Systems;
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Abstract:
One of the most fundamental challenges in computer vision is pedestrian detection since it involves both the classification and localization of pedestrians at a location. To achieve real-time pedestrian detection without having any loss in detection accuracy, an Optimized MobileNet + SSD network is proposed. There are four important components in pedestrian detection: feature extraction, deformation, occlusion handling and classification. The existing methods design these components either independently or in a sequential format, and the interaction among these components has not been explored yet. The proposed network lets the components work in coordination in such a manner that their strengths are improved and the number of parameters is decreased compared to recent detection architectures. We propose a concatenation feature fusion module for adding contextual information in the Optimized MobileNet + SSD network to improve the detection accuracy of pedestrians.
Keywords:
Caltech pedestrian dataset; Computer vision (CV); Jetson Nano board; Optimized MobileNet + SSD Network; Pedestrian detection

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