NDLI: Multiple deep features learning for object retrieval in surveillance videos

Content Provider	IET Digital Library
Author	Guo, Haiyun Wang, Jinqiao Lu, Hanqing
Abstract	Efficient indexing and retrieving objects of interest from large-scale surveillance videos are a significant and challenging topic. In this study, the authors present an effective multiple deep features learning approach for object retrieval in surveillance videos. Based on the discriminative convolutional neural network (CNN), they can learn multiple deep features to comprehensively describe the visual object. To be specific, they utilise the CNN model pre-trained on ImageNet ILSVRC12 and fine-tuned on our dataset to abstract structure information. In addition, they train another CNN model supervised by 11 colour names to deliver the colour information. To improve the retrieval performance, the deep features are encoded into short binary codes by locality-sensitive hash and fused to fast retrieve the object of interest. Retrieval experiments are performed on a dataset of 100k objects extracted from multi-camera surveillance videos. Comparison results with other common visual features show the effectiveness of the proposed approach.
Starting Page	268
Ending Page	272
Page Count	5
ISSN	17519632
Volume Number	10
e-ISSN	17519640
Issue Number	Issue 4, Jun (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/10/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2015.0291
Journal	IET Computer Vision
Publisher Date	2016-02-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Binary Codes CNN Model Colour Information Computer Vision And Image Processing Technique Discriminative Convolutional Neural Network Feature Extraction Feedforward Neural Network File Organisation Image And Video Coding Image Coding Image Colour Analysis Image Fusion Image Recognition ImageNet ILSVRC12 Indexing Information Analysis And Indexing Information Retrieval Technique Large-scale Surveillance Videos Locality-sensitive Hash Multiple Deep Feature Learning Neural Computing Technique Object Indexing Object Retrieval Retrieval Performance Improvement Sensor Fusion Short Binary Code Video Retrieval Video Signal Processing Video Surveillance
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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