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] |