Seek and You Will Find: A New Optimized Framework for Efficient Detection of Pedestrian

TL;DR

This paper introduces a new deep learning framework for pedestrian detection that balances speed and accuracy, utilizing a novel dataset and achieving significant performance improvements over existing methods.

Contribution

The paper presents a simple yet effective deep learning strategy and a new annotated dataset to enhance pedestrian detection, especially for small-sized pedestrians.

Findings

Achieved nearly 16% improvement over state-of-the-art methods.

Developed a new dataset with over 80K annotated pedestrians.

Demonstrated improved detection performance on multiple datasets.

Abstract

Studies of object detection and localization, particularly pedestrian detection have received considerable attention in recent times due to its several prospective applications such as surveillance, driving assistance, autonomous cars, etc. Also, a significant trend of latest research studies in related problem areas is the use of sophisticated Deep Learning based approaches to improve the benchmark performance on various standard datasets. A trade-off between the speed (number of video frames processed per second) and detection accuracy has often been reported in the existing literature. In this article, we present a new but simple deep learning based strategy for pedestrian detection that improves this trade-off. Since training of similar models using publicly available sample datasets failed to improve the detection performance to some significant extent, particularly for the instances of pedestrians of smaller sizes, we have developed a new sample dataset consisting of more than 80K annotated pedestrian figures in videos recorded under varying traffic conditions. Performance of the proposed model on the test samples of the new dataset and two other existing datasets, namely Caltech Pedestrian Dataset (CPD) and CityPerson Dataset (CD) have been obtained. Our proposed system shows nearly 16\% improvement over the existing state-of-the-art result.