Person Re-Identification with Deep Features and Transfer Learning

Wang, Shengke; Wu, Shan; Duan, Lianghua; Yu, Changyin; Sun, Yu; Dong, Junyu

doi:10.1109/cse-euc.2017.136

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…We also guarantee that there are not too many similar frames for the same video scene, which prevents overfitting on a few video scenes with a large number of frames. Note that the number of data is sufficient and is consistent with previous models using transfer learning that have 40-1000 frames of domain data [58][59][60].…”

Section: Training Datasetssupporting

confidence: 78%

“…Transfer learning is a popular technique to bring pre-trained deep learning models into other expert domains that significantly reduces the amount of required training data. This technique has been successfully applied to problems with very small domain datasets [58,59]. A similar strategy has also been adopted in the training of saliency detection for 360 images [31,60].…”

Section: Network Architecturementioning

confidence: 99%

See 1 more Smart Citation

Enhancing 360 Video Streaming through Salient Content in Head-Mounted Displays

Nguyen

Yan

2023

Sensors

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Predicting where users will look inside head-mounted displays (HMDs) and fetching only the relevant content is an effective approach for streaming bulky 360 videos over bandwidth-constrained networks. Despite previous efforts, anticipating users’ fast and sudden head movements is still difficult because there is a lack of clear understanding of the unique visual attention in 360 videos that dictates the users’ head movement in HMDs. This in turn reduces the effectiveness of streaming systems and degrades the users’ Quality of Experience. To address this issue, we propose to extract salient cues unique in the 360 video content to capture the attentive behavior of HMD users. Empowered by the newly discovered saliency features, we devise a head-movement prediction algorithm to accurately predict users’ head orientations in the near future. A 360 video streaming framework that takes full advantage of the head movement predictor is proposed to enhance the quality of delivered 360 videos. Practical trace-driven results show that the proposed saliency-based 360 video streaming system reduces the stall duration by 65% and the stall count by 46%, while saving 31% more bandwidth than state-of-the-art approaches.

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Section: Training Datasetssupporting

confidence: 78%

Section: Network Architecturementioning

confidence: 99%

Enhancing 360 Video Streaming through Salient Content in Head-Mounted Displays

Nguyen

Yan

2023

Sensors

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“…Wang et al [41] proposed a pairwise Siamese model by embedding a metric learning method at the top of the network to learn spatio-temporal features. The network takes a pair of images in order to obtain CNN features, and outputs whether two images belong to the same person or different one by employing the quadratic discriminant analysis method.…”

Section: Convolutional Neural Network Convolutional Neural Networkmentioning

confidence: 99%

Survey on Deep Learning Techniques for Person Re-Identification Task

Lavi¹,

Serj²,

Ullah³

2018

Preprint

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Intelligent video-surveillance is currently an active research field in computer vision and machine learning techniques. It provides useful tools for surveillance operators and forensic video investigators. Person reidentification (PReID) is one among these tools. It consists of recognizing whether an individual has already been observed over a camera in a network or not. This tool can also be employed in various possible applications such as off-line retrieval of all the video-sequences showing an individual of interest whose image is given a query, and online pedestrian tracking over multiple camera views. To this aim, many techniques have been proposed to increase the performance of PReID. Among the systems, many researchers utilized deep neural networks (DNNs) because of their better performance and fast execution at test time. Our objective is to provide for future researchers the work being done on PReID to date. Therefore, we summarized state-of-the-art DNN models being used for this task. A brief description of each model along with their evaluation on a set of benchmark datasets is given. Finally, a detailed comparison is provided among these models followed by some limitations that can work as guidelines for future research.

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