D-STC: Deep learning with spatio-temporal constraints for train drivers detection from videos

Combining LSTM and DenseNet for Automatic Annotation and Classification of Chest X-Ray Images

“…One direction is to achieve a finer granularity in annotation in different areas of the lungs and complement the analysis of overlap area cutting. Another direction is to use the target detection methods [34] in other fields to apply migration learning to chest disease detection.…”

Sustainability Analysis and Market Demand Estimation in the Retail Industry through a Convolutional Neural Network

“…Over fitting can also be solved, which makes the deep learning technology more robust and accurate than traditional methods. Deep learning technology has been widely used in several domains, such as automobile driving [33], image classification [34], and energy management [35], and it is proven to be effective in solving the problems on prediction and classification tasks. Deep learning, which is a subset of machine learning algorithms, is a technology to form a kind of network consisting of synapses that interconnect neurons and contains an activation function.…”

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

“…Table 3 list the frequency of most frequently used keywords. [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 ...…”