Video Based Human Activity Detection, Recognition and Classification of actions using SVM
Human motion analysis which includes activity detection and action recognition is currently gaining attention from computer vision researchers. Automatic monitoring of human activities and actions using computers has found significant applications in video surveillance, monitoring of patients and sports applications. With the tremendous advancement and development in digital video libraries, automatic interpretation of videos will save human effort in analysis and interpretation. This has led to the development of robust techniques in the field of computer vision. Human activity detection and recognition includes detection of human, tracking of human and recognition of actions. In this paper, detection of human is done using Gaussian Mixture Model, tracking is done using optical flow, recognition and classification of actions is done using SVM Classifier. The experiment is carried out with two public datasets KTH and Weizmann which are the videos with constant background. The classification accuracy for KTH dataset is 92.48% and for Weizmann dataset the classification accuracy is 93.64%.
(1) Dadi, H.S., Pillutla, G.K.M. & Makkena, M.L. Ann. Data. Sci. (2018) 5: 157. https://doi.org/10.1007/S40745-017-0123-2
(2) Harihara Santosh Dadi, Gopala Krishna Mohan Pillutla, Madhavi Latha Makkena. "Face Recognition and Human Tracking using GMM, HOG and SVM In Surveillance Videos", Annals of Data Science, 2017
(3) Kale, Kiran & Pawar, Sushant & Dhulekar, Pravin. (2015). Moving Object Tracking Using Optical Flow and Motion Vector Estimation. 1-6. 10.1109/Icrito.2015.7359323.
(4) Kiran Kale, Sushant Pawar, Pravin Dhulekar. "Moving object tracking
using optical flow and motion vector estimation", 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), 2015
(5) Jin, Ruichen, and Jongweon Kim. "Tracking feature extraction techniques with improved SIFT for video identification", Multimedia Tools and Applications, 2015.
(6) Mona M.Moussa, Elsayed Hamayed ,Magda B.Fayek, Heba A.El Nemr, An Enhanced Method For Human Action Recognition, Journal of Advanced Research, Volume 6, Issue 2, March 2015, Pages 163-169
(7) Dhulavvagol P.M., Kundur N.C. (2018) Human Action Detection and Recognition using SIFT and SVM. In: Nagabhushan T., Aradhya V., Jagadeesh P., Shukla S., M.L. C. (eds) Cognitive Computing And Information Processing. CCIP 2017. Communications in Computer and
Information Science, Vol 801. Springer, Singapore
(8) "Cognitive Computing and Information Processing", Springer Nature, 2018
(9) Santosh Kumar, Sanjay Kumar Singh. "Automatic identification of cattle using muzzle point pattern: a hybrid feature extraction and classification paradigm", Multimedia Tools and Applications, 2016
(10) S. Aadhirai, D. Najumnissa Jamal. "Feature extraction and analysis of renal abnormalities using fuzzy clustering segmentation and SIFT method", 2017 Third International Conference on Biosignals, Images and Instrumentation (ICBSII), 2017
(11) Davar Giveki, Mohammad Ali Soltanshahi, Gholam Ali Montazer. "A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern", Optik – International Journal for Light and Electron Optics, 2017
(12) C. Schuldt, L. Laptev, and B. Caputo,“Recognizing human actions a local SVM approach” In ICPR, 2004
(13) Junior, Oswaldo Ludwig, et al, “Trainable classifier-fusion schemes: An application to pedestrian detection, ”Intelligent Transportation Systems, 2009. ITSC’09. 12th International IEEE Conference on. IEEE, 2009.
(14) Hassaan Ali Qazi, Umar Jahangir, Bilal M Yousuf, Aqib Noor. "Human action recognition using SIFT and HOG method", 2017 International Conference on Information and Communication Technologies (ICICT),
(15) M. N. Al-Berry, Mohammed A.-M. Salem, H. M. Ebeid, A. S. Hussein, Mohamed F. Tolba. "chapter 14 Directional Multi-Scale Stationary Wavelet-Based Representation for Human Action Classification", IGI Global, 2017