Rameswar Panda obtained his Ph.D. in Electrical and Computer Engineering from University of California, Riverside. Prior to joining UC Riverside, he obtained his M.S. from Jadavpur University and B.E. from Biju Patnaik University of Technology, both in India. While studying for his Ph.D., Rameswar worked at NEC Labs America, Adobe Research, and Siemens Corporate Research. His primary research interests span the areas of computer vision, machine learning, and multimedia. In particular, his current focus is on making AI systems more efficient, i.e., developing novel deep learning methods that can operate with less human-annotated data (data efficient) and less computation (model efficient). He is also interested in image/video understanding, unsupervised/self-supervised representation learning, and multimodal learning (e.g., combining vision, sound/speech and language). His work has been published in top-tier conferences such as CVPR, ICCV, ECCV, NeurIPS, ICML, and ICLR, as well as highimpact journals such as TIP and TMM. He actively participates as a program committee member for many top AI conferences and was leading co-chair of the workshop on Multi-modal Video Analysis at ICCV 2019, ECCV 2020 and Workshop on Neural Architecture Search at CVPR 2020, CVPR 2021.Amit K. Roy-Chowdhury received his Ph.D. from the University of Maryland, College Park(UMCP) in 2002 and joined the University of California, Riverside (UCR) in 2004 where he is a Professor and Bourns Family Faculty Fellow of Electrical and Computer Engineering, Director of the Center for Robotics and Intelligent Systems, and Cooperating Faculty in the department of Computer Science and Engineering. He leads the Video Computing Group at UCR, working on foundational principles of computer vision, image processing, and statistical learning, with applications in cyber-physical, autonomous, and intelligent systems. He co-directs the US Department of Defense Center of Excellence NC4: Networked, Configurable Command, Control and Communications for Rapid Situational Awareness. He has published over 200 papers in peer-reviewed journals and conferences, and he is the first author of the book Camera Networks: The Acquisition and Analysis of Videos Over Wide Areas. He is on the editorial boards of major journals and program committees of the main conferences in his area. His students have been first authors on multiple papers that received Best Paper Awards at major international conferences, including ICASSP and ICMR. He is a Fellow of the IEEE and IAPR, received the Doctoral Dissertation Advising/Mentoring Award 2019 from UCR, and the ECE Distinguished Alumni Award from UMCP.

• Preface.- Person Re-identification: An Overview.- Supervised Re-identification: Optimizing the Annotation Effort.- Towards Unsupervised Person Re-identification.- Re-identification in Dynamic Camera Networks.- Future Research Directions.- Bibliography.- Authors' Biographies.

Rameswar Panda obtained his Ph.D. in Electrical and Computer Engineering from University of California, Riverside. Prior to joining UC Riverside, he obtained his M.S. from Jadavpur University and B.E. from Biju Patnaik University of Technology, both in India. While studying for his Ph.D., Rameswar worked at NEC Labs America, Adobe Research, and Siemens Corporate Research. His primary research interests span the areas of computer vision, machine learning, and multimedia. In particular, his current focus is on making AI systems more efficient, i.e., developing novel deep learning methods that can operate with less human-annotated data (data efficient) and less computation (model efficient). He is also interested in image/video understanding, unsupervised/self-supervised representation learning, and multimodal learning (e.g., combining vision, sound/speech and language). His work has been published in top-tier conferences such as CVPR, ICCV, ECCV, NeurIPS, ICML, and ICLR, as well as highimpact journals such as TIP and TMM. He actively participates as a program committee member for many top AI conferences and was leading co-chair of the workshop on Multi-modal Video Analysis at ICCV 2019, ECCV 2020 and Workshop on Neural Architecture Search at CVPR 2020, CVPR 2021.Amit K. Roy-Chowdhury received his Ph.D. from the University of Maryland, College Park(UMCP) in 2002 and joined the University of California, Riverside (UCR) in 2004 where he is a Professor and Bourns Family Faculty Fellow of Electrical and Computer Engineering, Director of the Center for Robotics and Intelligent Systems, and Cooperating Faculty in the department of Computer Science and Engineering. He leads the Video Computing Group at UCR, working on foundational principles of computer vision, image processing, and statistical learning, with applications in cyber-physical, autonomous, and intelligent systems. He co-directs the US Department of Defense Center of Excellence NC4: Networked, Configurable Command, Control and Communications for Rapid Situational Awareness. He has published over 200 papers in peer-reviewed journals and conferences, and he is the first author of the book Camera Networks: The Acquisition and Analysis of Videos Over Wide Areas. He is on the editorial boards of major journals and program committees of the main conferences in his area. His students have been first authors on multiple papers that received Best Paper Awards at major international conferences, including ICASSP and ICMR. He is a Fellow of the IEEE and IAPR, received the Doctoral Dissertation Advising/Mentoring Award 2019 from UCR, and the ECE Distinguished Alumni Award from UMCP.

• Preface.- Person Re-identification: An Overview.- Supervised Re-identification: Optimizing the Annotation Effort.- Towards Unsupervised Person Re-identification.- Re-identification in Dynamic Camera Networks.- Future Research Directions.- Bibliography.- Authors' Biographies.