Human Body Parts Estimation and Detection for Physical Sports Movements

The main purpose of human body part detection is to estimate the size, orientation or position of the human body parts within the digital scene information. Estimation of various body parts of the human from an image is a critical step for several model-based systems and body-parts tracking. In this paper, body parts detection for pose estimation is implemented. During foreground silhouettes detection, the proposed method have used segmentation techniques to obtained salient region areas and skin tone detection. After successful silhouettes extraction, body parts estimation is applied by using body parts model. Five basic body key points was determined and in addition seven more body sub key points was estimated with the help of five basic body key points. The estimated key points of the body are then represented using circular marks on the original image. The experimental results over two challenging video datasets such as KTH-multiview football and UCF sports action datasets showed significant accuracies of 90.01% and 86.67. The proposed method performs better than the state-of the-art methods in terms of body-parts detection accuracy.

[1]  Ahmad Jalal,et al.  Collaboration Achievement along with Performance Maintenance in Video Streaming , 2007 .

[2]  Daijin Kim,et al.  Depth silhouettes context: A new robust feature for human tracking and activity recognition based on embedded HMMs , 2015, 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[3]  Ahmad Jalal,et al.  A Complexity Removal in the Floating Point and Rate Control Phenomenon , 2005 .

[4]  Peyman Milanfar,et al.  A Tour of Modern Image Filtering: New Insights and Methods, Both Practical and Theoretical , 2013, IEEE Signal Processing Magazine.

[5]  Tae-Seong Kim,et al.  Recognition of Human Home Activities via Depth Silhouettes and ℜ Transformation for Smart Homes , 2012 .

[6]  Ahmad Jalal,et al.  Dense depth maps-based human pose tracking and recognition in dynamic scenes using ridge data , 2014, 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[7]  A. Jalal,et al.  Assembled algorithm in the real-time H.263 codec for advanced performance , 2005, Proceedings of 7th International Workshop on Enterprise networking and Computing in Healthcare Industry, 2005. HEALTHCOM 2005..

[8]  Sangho Park,et al.  Segmentation and tracking of interacting human body parts under occlusion and shadowing , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[9]  Peter R. Innocent,et al.  Determining and classifying the region of interest in ultrasonic images of the breast using neural networks , 1996, Artif. Intell. Medicine.

[10]  Daijin Kim,et al.  Human daily activity recognition with joints plus body features representation using Kinect sensor , 2015, 2015 International Conference on Informatics, Electronics & Vision (ICIEV).

[11]  Daijin Kim,et al.  A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments , 2014, Sensors.

[12]  Takayuki Nakata,et al.  Gaze Estimation Using Human Joint Rotation Angel , 2015, 2015 International Conference on Cyberworlds (CW).

[13]  Lihong Zheng,et al.  Facial expression recognition using hybrid features and self-organizing maps , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).

[14]  Daijin Kim,et al.  Ridge body parts features for human pose estimation and recognition from RGB-D video data , 2014, Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[15]  Daijin Kim,et al.  Human Depth Sensors-Based Activity Recognition Using Spatiotemporal Features and Hidden Markov Model for Smart Environments , 2016, J. Comput. Networks Commun..

[16]  Tae-Seong Kim,et al.  Human Activity Recognition via Recognized Body Parts of Human Depth Silhouettes for Residents Monitoring Services at Smart Home , 2013 .

[17]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Damla Arifoglu,et al.  Activity Recognition and Abnormal Behaviour Detection with Recurrent Neural Networks , 2017, FNC/MobiSPC.

[19]  Dao Nam Anh Detection of lesion region in skin images by moment of patch , 2016, 2016 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF).

[20]  Shaharyar Kamal,et al.  Improved Behavior Monitoring and Classification Using Cues Parameters Extraction from Camera Array Images , 2019, Int. J. Interact. Multim. Artif. Intell..

[21]  Shaharyar Kamal,et al.  Real-time life logging via a depth silhouette-based human activity recognition system for smart home services , 2014, 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[22]  Mian Ahmad Zeb,et al.  Security and QoS Optimization for Distributed Real Time Environment , 2007, 7th IEEE International Conference on Computer and Information Technology (CIT 2007).

[23]  Tae-Seong Kim,et al.  Human Activity Recognition via the Features of Labeled Depth Body Parts , 2012, ICOST.

[24]  Ami Luttwak,et al.  Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements , 2013, Sensors.

[25]  Daijin Kim,et al.  Depth map-based human activity tracking and recognition using body joints features and Self-Organized Map , 2014, Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[26]  S. Maheswari,et al.  Enhanced skin tone detection using heuristic thresholding , 2017 .

[27]  Doo-Kwon Baik,et al.  A Context-Aware Fitness Guide System for Exercise Optimization in U-Health , 2009, IEEE Transactions on Information Technology in Biomedicine.

[28]  Ahmad Jalal,et al.  The Mechanism of Edge Detection using the Block Matching Criteria for the Motion Estimation , 2005 .

[29]  Cesar A. Azurdia-Meza,et al.  Depth Maps-Based Human Segmentation and Action Recognition Using Full-Body Plus Body Color Cues Via Recognizer Engine , 2019, Journal of Electrical Engineering & Technology.

[30]  Yuan Luo,et al.  Human limb motion real-time tracking based on CamShift for intelligent rehabilitation system , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[31]  Ahmad Jalal,et al.  Security Enhancement for E-Learning Portal , 2008 .

[32]  Shaharyar Kamal,et al.  A Hybrid Feature Extraction Approach for Human Detection, Tracking and Activity Recognition Using Depth Sensors , 2016 .

[33]  Yan Zhang,et al.  On the Euclidean distance of images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Daijin Kim,et al.  A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems , 2017, Int. J. Interact. Multim. Artif. Intell..

[35]  Dong-Seong Kim,et al.  Detecting Complex 3D Human Motions with Body Model Low-Rank Representation for Real-Time Smart Activity Monitoring System , 2018, KSII Trans. Internet Inf. Syst..

[36]  Ahmad Jalal,et al.  Global Security Using Human Face Understanding under Vision Ubiquitous Architecture System , 2008 .

[37]  A. Jalal,et al.  Security Architecture for Third Generation (3G) using GMHS Cellular Network , 2007, 2007 International Conference on Emerging Technologies.

[38]  Stefan Carlsson,et al.  3D Pictorial Structures for Multiple View Articulated Pose Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[39]  Joris De Schutter,et al.  An adaptable system for RGB-D based human body detection and pose estimation , 2014, J. Vis. Commun. Image Represent..

[40]  Antoni B. Chan,et al.  3D Human Pose Estimation from Monocular Images with Deep Convolutional Neural Network , 2014, ACCV.

[41]  Sangwook Kim,et al.  Algorithmic implementation and efficiency maintenance of real-time environment using low-bitrate wireless communication , 2006, The Fourth IEEE Workshop on Software Technologies for Future Embedded and Ubiquitous Systems, and the Second International Workshop on Collaborative Computing, Integration, and Assurance (SEUS-WCCIA'06).

[42]  Ahmad Jalal,et al.  Wearable Sensor-Based Human Behavior Understanding and Recognition in Daily Life for Smart Environments , 2018, 2018 International Conference on Frontiers of Information Technology (FIT).

[43]  Tae-Seong Kim,et al.  Depth video-based human activity recognition system using translation and scaling invariant features for life logging at smart home , 2012, IEEE Transactions on Consumer Electronics.

[44]  Ahmad Jalal,et al.  Multiple Facial Feature Detection Using Vertex-Modeling Structure , 2007 .

[45]  Daijin Kim,et al.  Shape and Motion Features Approach for Activity Tracking and Recognition from Kinect Video Camera , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops.

[46]  Daijin Kim,et al.  Individual detection-tracking-recognition using depth activity images , 2015, 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[47]  Maria Mahmood,et al.  Multi-features descriptors for Human Activity Tracking and Recognition in Indoor-Outdoor Environments , 2019, 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST).

[48]  Ram Nevatia,et al.  Body Part Detection for Human Pose Estimation and Tracking , 2007, 2007 IEEE Workshop on Motion and Video Computing (WMVC'07).

[49]  Ahmad Jalal,et al.  Robust Spatio-Temporal Features for Human Interaction Recognition Via Artificial Neural Network , 2018, 2018 International Conference on Frontiers of Information Technology (FIT).

[50]  Daijin Kim,et al.  Robust human activity recognition from depth video using spatiotemporal multi-fused features , 2017, Pattern Recognit..

[51]  Mubarak Shah,et al.  Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[52]  Ahmad Jalal,et al.  Facial Expression Recognition in Image Sequences Using 1D Transform and Gabor Wavelet Transform , 2018, 2018 International Conference on Applied and Engineering Mathematics (ICAEM).

[53]  David S. Monaghan,et al.  3D Human Gait Reconstruction and Monitoring Using Body-Worn Inertial Sensors and Kinematic Modeling , 2016, IEEE Sensors Journal.

[54]  Mircea Nicolescu,et al.  Human Body Parts Tracking Using Torso Tracking: Applications to Activity Recognition , 2012, 2012 Ninth International Conference on Information Technology - New Generations.

[55]  Shaharyar Kamal,et al.  Dense RGB-D Map-Based Human Tracking and Activity Recognition using Skin Joints Features and Self-Organizing Map , 2015, KSII Trans. Internet Inf. Syst..

[56]  Ahmad Jalal,et al.  Advanced Performance Achievement using Multi- Algorithmic Approach of Video Transcoder for Low Bitrate Wireless Communication , 2005 .