Random Occlusion-recovery for Person Re-identification
As a basic task of multi-camera surveillance system, person re-identification aims to re-identify a query pedestrian observed from non-overlapping multiple cameras or across different time with a single camera. Recently, deep learning-based person re-identification models have achieved great success in many benchmarks. However, these supervised models require a large amount of labeled image data, and the process of manual labeling spends much manpower and time. In this study, we introduce a method to automatically synthesize labeled person images and adopt them to increase the sample number per identity for person re-identification datasets. To be specific, we use block rectangles to randomly occlude pedestrian images. Then, a generative adversarial network (GAN) model is proposed to use paired occluded and original images to synthesize the de-occluded images that similar but not identical to the original image. Afterwards, we annotate the de-occluded images with the same labels of their corresponding raw images and use them to augment the number of samples per identity. Finally, we use the augmented datasets to train baseline model. The experiment results on CUHK03, Market-1501 and DukeMTMC-reID datasets show that the effectiveness of the proposed method.
Code (0)
등록된 구현이 없습니다.
Tasks
Generative Adversarial NetworkPerson Re-IdentificationSimilar Papers 제목 키워드 기반
Learning Feature Recovery Transformer for Occluded Person Re-identification
One major issue that challenges person re-identification (Re-ID) is the ubiquitous occlusion over the captured persons. There are two main challenges for the occluded person Re-ID problem, i.e., the interference of noise…
Graph MatchingGraph SimilarityOccluded Person Re-IdentificationPerson Re-IdentificationHierarchical and Efficient Learning for Person Re-Identification
Recent works in the person re-identification task mainly focus on the model accuracy while ignore factors related to the efficiency, e.g. model size and latency, which are critical for practical application. In this pape…
Person Re-IdentificationExploring Stronger Transformer Representation Learning for Occluded Person Re-Identification
Due to some complex factors (e.g., occlusion, pose variation and diverse camera perspectives), extracting stronger feature representation in person re-identification remains a challenging task. In this paper, we proposed…
Contrastive LearningOccluded Person Re-IdentificationPerson Re-IdentificationRepresentation LearningErasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification
Occlusion perturbation presents a significant challenge in person re-identification (re-ID), and existing methods that rely on external visual cues require additional computational resources and only consider the issue o…
Adversarial DefenseOccluded Person Re-IdentificationPerson Re-IdentificationContent-Adaptive Auto-Occlusion Network for Occluded Person Re-Identification
The occluded person re-identification (ReID) aims to match person images captured in severely occluded environments. Current occluded ReID works mostly rely on auxiliary models or employ a part-to-part matching strategy.…
Occluded Person Re-IdentificationPerson Re-Identification