PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection

TL;DR

This paper introduces PIDNet, a novel efficient neural network designed for real-time dynamic pedestrian intrusion detection from moving cameras, addressing the challenges of changing AoIs and multiple pedestrians.

Contribution

The paper proposes PIDNet with three innovative design modules and establishes the first benchmark dataset for this task, advancing the field of vision-based dynamic PID.

Findings

Achieves 67.1% PID accuracy on the new dataset.

Runs at 9.6 fps, enabling real-time applications.

Provides a baseline for future research in dynamic pedestrian intrusion detection.

Abstract

Vision-based dynamic pedestrian intrusion detection (PID), judging whether pedestrians intrude an area-of-interest (AoI) by a moving camera, is an important task in mobile surveillance. The dynamically changing AoIs and a number of pedestrians in video frames increase the difficulty and computational complexity of determining whether pedestrians intrude the AoI, which makes previous algorithms incapable of this task. In this paper, we propose a novel and efficient multi-task deep neural network, PIDNet, to solve this problem. PIDNet is mainly designed by considering two factors: accurately segmenting the dynamically changing AoIs from a video frame captured by the moving camera and quickly detecting pedestrians from the generated AoI-contained areas. Three efficient network designs are proposed and incorporated into PIDNet to reduce the computational complexity: 1) a special PID task backbone for feature sharing, 2) a feature cropping module for feature cropping, and 3) a lighter detection branch network for feature compression. In addition, considering there are no public datasets and benchmarks in this field, we establish a benchmark dataset to evaluate the proposed network and give the corresponding evaluation metrics for the first time. Experimental results show that PIDNet can achieve 67.1% PID accuracy and 9.6 fps inference speed on the proposed dataset, which serves as a good baseline for the future vision-based dynamic PID study.