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Table 1 Title of 25 papers published in ScienceDirect

From: Intelligent video surveillance: a review through deep learning techniques for crowd analysis

1 SVAS: Surveillance Video Analysis System [1]
2 Jointly learning perceptually heterogeneous features for blind 3D video quality assessment [2]
3 Learning to detect video events from zero or very few video examples [3]
4 Learning an event-oriented and discriminative dictionary based on an adaptive label-consistent K-SVD method for event detection in soccer videos [4]
5 Towards efficient and objective work sampling: Recognizing workers’ activities in site surveillance videos with two-stream convolutional networks [5]
6 Dairy goat detection based on Faster R-CNN from surveillance video [6]
7 Performance evaluation of deep feature learning for RGB-D image/video classification [7]
8 Surveillance scene representation and trajectory abnormality detection using aggregation of multiple concepts [8]
9 Human Action Recognition using 3D convolutional neural networks with 3D Motion Cuboids in Surveillance Videos [9]
10 Neural networks based visual attention model for surveillance videos [10]
11 Application of deep learning for object detection [11]
12 A study of deep convolutional auto-encoders for anomaly detection in videos [12]
13 A novel deep multi-channel residual networks-based metric learning method for moving human localization in video surveillance [13]
14 Video surveillance systems-current status and future trends [14]
15 Enhancing transportation systems via deep learning: a survey [15]
16 Pedestrian tracking by learning deep features [16]
17 Action recognition using spatial-optical data organization and sequential learning framework [17]
18 Video pornography detection through deep learning techniques and motion information [18]
19 Deep learning to frame objects for visual target tracking [19]
20 Boosting deep attribute learning via support vector regression for fast moving crowd counting [20]
21 D-STC: deep learning with spatio-temporal constraints for train drivers detection from videos [21]
22 A robust human activity recognition system using smartphone sensors and deep learning [22]
23 Regional deep learning model for visual tracking [23]
24 Fog computing enabled cost-effective distributed summarization of surveillance videos for smart cities [24]
25 SIFT and tensor based object detection and classification in videos using deep neural networks [25]