3 research outputs found
Cross Domain Knowledge Learning with Dual-branch Adversarial Network for Vehicle Re-identification
The widespread popularization of vehicles has facilitated all people's life
during the last decades. However, the emergence of a large number of vehicles
poses the critical but challenging problem of vehicle re-identification (reID).
Till now, for most vehicle reID algorithms, both the training and testing
processes are conducted on the same annotated datasets under supervision.
However, even a well-trained model will still cause fateful performance drop
due to the severe domain bias between the trained dataset and the real-world
scenes.
To address this problem, this paper proposes a domain adaptation framework
for vehicle reID (DAVR), which narrows the cross-domain bias by fully
exploiting the labeled data from the source domain to adapt the target domain.
DAVR develops an image-to-image translation network named Dual-branch
Adversarial Network (DAN), which could promote the images from the source
domain (well-labeled) to learn the style of target domain (unlabeled) without
any annotation and preserve identity information from source domain. Then the
generated images are employed to train the vehicle reID model by a proposed
attention-based feature learning model with more reasonable styles. Through the
proposed framework, the well-trained reID model has better domain adaptation
ability for various scenes in real-world situations. Comprehensive experimental
results have demonstrated that our proposed DAVR can achieve excellent
performances on both VehicleID dataset and VeRi-776 dataset.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0786
Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification
Existing vehicle re-identification methods commonly use spatial pooling
operations to aggregate feature maps extracted via off-the-shelf backbone
networks. They ignore exploring the spatial significance of feature maps,
eventually degrading the vehicle re-identification performance. In this paper,
firstly, an innovative spatial graph network (SGN) is proposed to elaborately
explore the spatial significance of feature maps. The SGN stacks multiple
spatial graphs (SGs). Each SG assigns feature map's elements as nodes and
utilizes spatial neighborhood relationships to determine edges among nodes.
During the SGN's propagation, each node and its spatial neighbors on an SG are
aggregated to the next SG. On the next SG, each aggregated node is re-weighted
with a learnable parameter to find the significance at the corresponding
location. Secondly, a novel pyramidal graph network (PGN) is designed to
comprehensively explore the spatial significance of feature maps at multiple
scales. The PGN organizes multiple SGNs in a pyramidal manner and makes each
SGN handles feature maps of a specific scale. Finally, a hybrid pyramidal graph
network (HPGN) is developed by embedding the PGN behind a ResNet-50 based
backbone network. Extensive experiments on three large scale vehicle databases
(i.e., VeRi776, VehicleID, and VeRi-Wild) demonstrate that the proposed HPGN is
superior to state-of-the-art vehicle re-identification approaches
Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking and Vehicle Re-ID in Multi-Camera Networks
As camera networks have become more ubiquitous over the past decade, the
research interest in video management has shifted to analytics on multi-camera
networks. This includes performing tasks such as object detection, attribute
identification, and vehicle/person tracking across different cameras without
overlap. Current frameworks for management are designed for multi-camera
networks in a closed dataset environment where there is limited variability in
cameras and characteristics of the surveillance environment are well known.
Furthermore, current frameworks are designed for offline analytics with
guidance from human operators for forensic applications. This paper presents a
teamed classifier framework for video analytics in heterogeneous many-camera
networks with adversarial conditions such as multi-scale, multi-resolution
cameras capturing the environment with varying occlusion, blur, and
orientations. We describe an implementation for vehicle tracking and vehicle
re-identification (re-id), where we implement a zero-shot learning (ZSL) system
that performs automated tracking of all vehicles all the time. Our evaluations
on VeRi-776 and Cars196 show the teamed classifier framework is robust to
adversarial conditions, extensible to changing video characteristics such as
new vehicle types/brands and new cameras, and offers real-time performance
compared to current offline video analytics approaches