People reidentification in surveillance and forensics

By Roberto Vezzani, Davide Baltieri, Rita Cucchiara. ACM Computing Surveys, 2013

298 citations recorded by the source corpus.

The field of surveillance and forensics research is currently shifting focus and is now showing an ever increasing interest in the task of people reidentification. This is the task of assigning the same identifier to all instances of a particular individual captured in a series of images or videos, even after the occurrence of significant gaps over time or space. People reidentification can be a useful tool for people analysis in security as a data association method for long-term tracking in surveillance. However, current identification techniques being utilized present many difficulties and shortcomings. For instance, they rely solely on the exploitation of visual cues such as color, texture, and the object’s shape. Despite the many advances in this field, reidentification is still an open problem. This survey aims to tackle all the issues and challenging aspects of people reidentification while simultaneously describing the previously proposed solutions for the encountered problems. This begins with the first attempts of holistic descriptors and progresses to the more recently adopted 2D and 3D model-based approaches. The survey also includes an exhaustive treatise of all the aspects of people reidentification, including available datasets, evaluation metrics, and benchmarking.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ed10cd3cc1a0383d7adc6dc5830fa4ef40b86158

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Qi Tian
2. Liang Zheng
3. Yi Yang
4. Shiliang Zhang
5. Shaogang Gong
6. Wen Gao
7. Tao A. Xiang
8. Chi Su

In Computer Vision and Pattern Recognition

1. Yi Yang
2. Qi Tian
3. Liang Zheng
4. Xiaogang Wang
5. Luc Van Gool
6. Tao A. Xiang
7. Shaogang Gong
8. Dacheng Tao

Citations in the corpus

• Unsupervised Cross-Dataset Transfer Learning for Person Re-identification (2016, background): “Despite the best efforts from the computer vision researchers [33, 10], it remains an unsolved problem.”
• Person Re-Identification Using Kernel-Based Metric Learning Methods (2014, methodology): “A good overview of existing re-ID methods can be found in [7, 10, 13, 23, 29] and references therein.”
• Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification (2016, background): “It has attracted increasing interests and encouraged considerable efforts in recent years due to its broad applications in video surveillance [34, 33].”
• Multi-Task Learning with Low Rank Attribute Embedding for Multi-Camera Person Re-Identification (2018, background): “There are several surveys [21], [22], [23] on person re-identification.”
• Person re-identification by Local Maximal Occurrence representation and metric learning (2014, background): “Many approaches have been proposed for person reidentification [40] [8], which greatly advance this field.”
• Person Re-identification Using the Silhouette Shape Described by a Point Distribution Model (2015, background): “These cues, especially colors, provide strong discriminative power [8, 14].”
• Learning a Discriminative Null Space for Person Re-identification (2016, background): “The problem of person re-identification (re-id) has attracted great attention in the past five years [34, 10].”