000			03768ntm a2200277 a 4500
001			vtls000101449
003			KUKTEM
005			20251117113346.0
008			171002t2017 my a f am 000 0 eng d
020			_aTHE0005165(Local)
039		9	_a201905131821 _bhanafiah _y201710021552 _znazri
040			_aUMP
090			_aFKEE .A94 2017 r Thesis
100	0		_aWan Nur Azhani W.Samsudin
245	1	0	_aImproved abnormal detection using self-adaptive social force model for visual surveillance / _cWan Nur Azhani Bt. W.Samsudin
260			_aKuantan, Pahang : _bUMP, _c2017
300			_axviii, 233 p. : _bill. (some col.) ; _c30 cm. + _e1 CD-ROM
500			_aFaculty of Electrical and Electronics Engineering
502			_aThesis (Doctor of Philosophy (Electrical Engineering)) -- Universiti Malaysia Pahang – 2017
504			_aBibliography : p. 121-133
520	3		_aWith the growth of technology in computer vision, there is a great demand for an automated surveillance system in replaced to the traditional visual surveillance. The automated surveillance system is a system that monitors the behavior and activities of the crowd whether it is normal or not. The abnormal detection in a crowd is a noteworthy research topic in automated surveillance system in public places. It is emergent to detect the abnormal events as quickly as possible and take appropriate actions to minimize the loss and ensure the public safety. In this work, we aim to find the significant interaction forces and detect the abnormality in the crowd by using Self-Adaptive Social Force Model. For this point, Horn-Schunck optical flow is used to get the flow vector for each pixel in the image frames. Instead of tracking individuals, particle advection is performed to capture continuity of crowd flow and its trajectories. These particles are then advected to a new location according to its underlying optical flow vector at the current location. Using the attained flow vectors from this stage, interaction force estimation is done based on SFM theory. This experiment is done with the hypothesis that high magnitude of interaction force portrayed the abnormal behaviour in a crowd. However, there is a problem with the earlier SFM, which is the similarity of actual velocity and desired velocity caused the abnormal detection inaccurate. The estimation of the good quality of interaction forces is critical in this case and has not been explored yet. So, Self-Adaptive SFM is developed in order to estimate a good quality of interaction forces since it is crucial to achieve better abnormal detection, which represents the behavior of the crowd. From the experiment, the highest and least magnitude of interaction force can be localized in the image frame. The proposed algorithm is validated with three challenging datasets contain abnormal videos, including the videos of crime in Malaysia. For both indoor and outdoor scene, the proposed algorithm outperforms the other methods with accuracy 97% and 100%. For the benchmarking datasets, the AUC (Area under Curve) score of the proposed algorithm is quite comparable with previous works with the score of 0.9916. The AUC score provided by the proposed algorithm on PETS2009 datasets is about 0.9026 and 0.9940 for Malaysia Crime dataset. Based on these results, it can conclude that the high magnitudes of interaction forces portray the abnormality in the scene and Self-Adaptive SFM is well-performed on crime scene with the rapid motion characteristic.
610	2	0	_aFaculty of Electrical and Electronics Engineering _xDissertations
650		0	_aUniversities and Colleges _xDissertations
650		0	_aTheses
999			_aVIRTUA40 _c7323 _d7329
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