Disparity Sliding Window: Object Proposals From Disparity Images

TL;DR

This paper introduces a disparity sliding window method that leverages depth information from stereo images to reduce the number of object candidates in detection tasks, maintaining accuracy while decreasing computational load.

Contribution

It presents a novel disparity-based sliding window technique and provides a theoretical analysis of candidate reduction, validated on pedestrian detection benchmarks.

Findings

Significantly reduces object candidates in sliding window detection.

Maintains detection accuracy comparable to traditional methods.

Demonstrates improved efficiency on KITTI benchmark.

Abstract

Sliding window approaches have been widely used for object recognition tasks in recent years. They guarantee an investigation of the entire input image for the object to be detected and allow a localization of that object. Despite the current trend towards deep neural networks, sliding window methods are still used in combination with convolutional neural networks. The risk of overlooking an object is clearly reduced compared to alternative detection approaches which detect objects based on shape, edges or color. Nevertheless, the sliding window technique strongly increases the computational effort as the classifier has to verify a large number of object candidates. This paper proposes a sliding window approach which also uses depth information from a stereo camera. This leads to a greatly decreased number of object candidates without significantly reducing the detection accuracy. A theoretical investigation of the conventional sliding window approach is presented first. Other publications to date only mentioned rough estimations of the computational cost. A mathematical derivation clarifies the number of object candidates with respect to parameters such as image and object size. Subsequently, the proposed disparity sliding window approach is presented in detail. The approach is evaluated on pedestrian detection with annotations and images from the KITTI object detection benchmark. Furthermore, a comparison with two state-of-the-art methods is made. Code is available in C++ and Python https://github.com/julimueller/ disparity-sliding-window.