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Deep‐learning‐based pedestrian detectors can enhance the capabilities of smart camera systems in a wide spectrum of machine vision applications including video surveillance, autonomous driving, robots and drones, smart factory, and health monitoring. However, such complex paradigms do not scale easily and are not traditionally implemented in resource‐constrained smart cameras for on‐device processing which offers significant advantages in situations when real‐time monitoring and privacy are vital. This work addresses the challenge of achieving a good trade‐off between accuracy and speed for efficient deep‐learning‐based pedestrian detection in smart camera applications. The contributions of this work are the following: 1) a computationally efficient architecture based on separable convolutions that integrates dense connections across layers and multi‐scale feature fusion to improve representational capacity while decreasing the number of parameters and operations, 2) a more elaborate loss function for improved localization, 3) and an anchor‐less approach for detection. The proposed approach referred to as YOLOpeds is evaluated using the PETS2009 surveillance dataset on 320 × 320 images. A real‐system implementation is presented using the Jetson TX2 embedded platform. YOLOpeds provides real‐time sustained operation of over 30 frames per second with detection rates in the range of 86% outperforming existing deep learning models.

YOLOpeds: efficient real‐time single‐shot pedestrian detection for smart camera applications