Vehicle Re-Identification: an Efficient Baseline Using Triplet Embedding

Abstract

In this paper we tackle the problem of vehicle re-identification in a camera network utilizing triplet embeddings. Re-identification is the problem of matching appearances of objects across different cameras. With the proliferation of surveillance cameras enabling smart and safer cities, there is an ever-increasing need to re-identify vehicles across cameras. Typical challenges arising in smart city scenarios include variations of viewpoints, illumination and self occlusions. Most successful approaches for re-identification involve (deep) learning an embedding space such that the vehicles of same identities are projected closer to one another, compared to the vehicles representing different identities. Popular loss functions for learning an embedding (space) include contrastive or triplet loss. In this paper we provide an extensive evaluation of these losses applied to vehicle re-identification and demonstrate that using the best practices for learning embeddings outperform most of the previous approaches proposed in the vehicle re-identification literature. Compared to most existing state-of-the-art approaches, our approach is simpler and more straightforward for training utilizing only identity-level annotations, along with one of the smallest published embedding dimensions for efficient inference. Furthermore in this work we introduce a formal evaluation of a triplet sampling variant (batch sample) into the re-identification literature.

Accepted at IJCNN 2019. This arxiv version adds result on newer datasets post conference submission

References in corpus (5)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• In Defense of the Triplet Loss for Person Re-Identification
• Recurrent Models of Visual Attention
• Working hard to know your neighbor's margins: Local descriptor learning loss
• Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals

Cited by in corpus (8)

• Stripe-based and Attribute-aware Network: A Two-Branch Deep Model for Vehicle Re-identification
• Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model
• Simulating Content Consistent Vehicle Datasets with Attribute Descent
• Part-Guided Attention Learning for Vehicle Instance Retrieval
• A unified neural network for object detection, multiple object tracking and vehicle re-identification
• Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking and Vehicle Re-ID in Multi-Camera Networks
• PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data
• Background Segmentation for Vehicle Re-Identification