Part-based Multi-stream Model for Vehicle Searching

TL;DR

This paper introduces a novel part-based multi-stream deep learning model that segments vehicles into discriminative patches to improve vehicle retrieval and re-identification accuracy, outperforming baseline methods.

Contribution

The paper presents a new method for identifying discriminative patches in vehicle images and designs a multi-stream network for enhanced vehicle search performance.

Findings

Outperforms baseline on VehicleID dataset

Effective segmentation of discriminative vehicle patches

Improved accuracy in vehicle re-identification

Abstract

Due to the enormous requirement in public security and intelligent transportation system, searching an identical vehicle has become more and more important. Current studies usually treat vehicle as an integral object and then train a distance metric to measure the similarity among vehicles. However, these raw images may be exactly similar to ones with different identification and include some pixels in background that may disturb the distance metric learning. In this paper, we propose a novel and useful method to segment an original vehicle image into several discriminative foreground parts, and these parts consist of some fine grained regions that are named discriminative patches. After that, these parts combined with the raw image are fed into the proposed deep learning network. We can easily measure the similarity of two vehicle images by computing the Euclidean distance of the features from FC layer. Two main contributions of this paper are as follows. Firstly, a method is proposed to estimate if a patch in a raw vehicle image is discriminative or not. Secondly, a new Part-based Multi-Stream Model (PMSM) is designed and optimized for vehicle retrieval and re-identification tasks. We evaluate the proposed method on the VehicleID dataset, and the experimental results show that our method can outperform the baseline.