* Sale Price for only Code / simulation – For Hardware / more Details contact : 8925533488
Objective: The objective of this project is to recognise and annotate the human action in an unconstrained environment, where the images contain a huge range of variability.
Given a video sequence, the task of action recognition is to identify the most similar action among the action sequences learned by the system. Such human action recognition is based on evidence gathered from videos. It has wide application including surveillance, video indexing, biometrics, telehealth, and human-computer interaction. Vision-based human action recognition is affected by several challenges due to view changes, occlusion, variation in execution rate, anthropometry, camera motion, and background clutter. In this survey, we provide an overview of the existing methods based on their ability to handle these challenges as well as how these methods can be generalized and their ability to detect abnormal actions. Such systematic classification will help researchers to identify the suitable methods available to address each of the challenges faced and their limitations. In addition, we also identify the publicly available datasets and the challenges posed by them. From this survey, we draw conclusions regarding how well a challenge has been solved, and we identify potential research areas that require further work.
- Appearance based methods involves LDA
- Geometric methods.
- In appearance based methods, less accurate of features description because of whole image consideration
- In geometric based methods, the geometric features like distance between eyes, face length and width, etc., are considered which not provides optimal results
- Adaboost Classifiers
- Detecting accuracy is high due to extracting local features of the image
- The geometric features like distance between eyes, face length and width, etc., are considered which provides high optimal results
- Queue forming
- People counting
- MATLAB 7.5 and above versions
 P. Ekman and W. V. Friesen, ?Facial action coding system,? 1977.
 J. Hager, P. Ekman, and W. Friesen, ?Facial action coding system. salt lake city, ut: A human face,? ISBN 0-931835-01-1, Tech. Rep., 2002.
 Z. Zhang, ?Feature-based facial expression recognition: Sensitivity analysisandexperimentswithamultilayerperceptron,?Internationaljournal of pattern recognition and Arti?cial Intelligence, vol. 13, no. 06, pp. 893?911, 1999.  G. Guo and C. R. Dyer, ?Simultaneous feature selection and classi?er training via linear programming: A case study for face expression recognition,? in Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, vol. 1. IEEE, 2003, pp. I?346.
 M. F. Valstar, I. Patras, and M. Pantic, ?Facial action unit detection using probabilistic actively learned support vector machines on tracked facial point data,? in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR?05)-Workshops. IEEE, 2005, pp. 76?76.
 M. Valstar and M. Pantic, ?Fully automatic facial action unit detection and temporal analysis,? in 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW?06). IEEE, 2006, pp. 149? 149.
 C. Shan, S. Gong, and P. W. McOwan, ?Facial expression recognition based on local binary patterns: A comprehensive study,? Image and Vision Computing, vol. 27, no. 6, pp. 803?816, 2009.
 C. Padgett and G. W. Cottrell, ?Representing face images for emotion classi?cation,? Advances in neural information processing systems, pp. 894?900, 1997.
 G. Donato, M. S. Bartlett, J. C. Hager, P. Ekman, and T. J. Sejnowski, ?Classifying facial actions,? IEEE Transactions on pattern analysis and machine intelligence, vol. 21, no. 10, pp. 974?989, 1999.
 A. J. Calder, A. M. Burton, P. Miller, A. W. Young, and S. Akamatsu, ?A principal component analysis of facial expressions,? Vision research, vol. 41, no. 9, pp. 1179?1208, 2001.