Unifying Person and Vehicle Re-Identification

Daniel Organisciak,Nauman Aslam,Hubert P H Shum,Edmond S L Ho,Dimitrios Sakkos

doi:10.1109/access.2020.3004092

Abstract

Person and vehicle re-identification (re-ID) are important challenges for the analysis of the burgeoning collection of urban surveillance videos. To efficiently evaluate such videos, which are populated with both vehicles and pedestrians, it would be preferable to have one unified framework with effective performance across both domains. Unfortunately, due to the contrasting composition of humans and vehicles, no architecture has yet been established that can adequately perform both tasks. We release a Person and Vehicle Unified Data Set (PVUD) comprising of both pedestrians and vehicles from popular existing re-ID data sets, in order to better model the data that we would expect to find in the real world. We exploit the generalisation ability of metric learning to propose a re-ID framework that can learn to re-identify humans and vehicles simultaneously. We design our network, MidTriNet, to harness the power of mid-level features to develop better representations for the re-ID tasks. We help the system to handle mixed data by appending unification terms with additional hard negative and hard positive mining to MidTriNet. We attain comparable accuracy training on PVUD to training on the comprising data sets separately, supporting the system's generalisation power. To further demonstrate the effectiveness of our framework, we also obtain results better than, or competitive with, the state-of-the-art on each of the Market-1501, CUHK03, VehicleID and VeRi data sets.

Highlights

Re-identification is a core challenge for the computer vision community whereby a detection is required to be matched with another detection of the same object, typically from a different viewpoint
Our data set and source code can be found at https://github.com/Person and Vehicle Unified Data Set (PVUD)
MidTriNet is the baseline TriNet model, with the addition of the design choices described in Section IV-B to harness mid-level features: reduction of the stride length in the conv4 block, and removal of the final two conv5 blocks

Summary

Introduction

Re-identification (re-ID) is a core challenge for the computer vision community whereby a detection is required to be matched with another detection of the same object, typically from a different viewpoint. We would prefer re-ID systems to be able to handle this occurrence for continuous tracking. For this reason, Wei et al [7] attempt to develop an integrated application by using existing person and vehicle re-ID architectures. Wei et al [7] attempt to develop an integrated application by using existing person and vehicle re-ID architectures This does not truly unify the tasks, as the system accuracy depends on sub-systems, which is not ioptimal.

Methods

Results

Conclusion