Skip to main content

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]