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To recognize criminal offenders and track them across different places. Major milestone to improve the automation process of visual surveillance in order The use of surveillance technology should without doubt assist to lessen the risksĪnd number of crimes by serving as a deterrent.

GALERI FOTO AL GHAZALI OFFLINE

Immense amount of CCTV footage recorded either offline or streamed at real time. Impossible regardless the size of manpower recruited to monitor and analyze the Law enforcement agencies can make use of surveillance systemsįor the safety of our neighborhood and crime prevention or resolving. Although CCTV are becoming ubiquitousĪnd intrusive being largely deployed to survey public and strategic areas such asĪirports, metro stations and shopping malls, the identification of suspects usingĪutomated methods is still a challenging task to stop further terrorist attacks or World has recently witnessed a surge of criminal and terrorist activities that took As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. The proposed gait recognition technique achieves significant accuracy across all appearances and views. We have evaluated the gait features and support vector machine classifier-based gait recognition on a locally collected gait dataset that captures the effect of view variance in high scene depth videos. Spatio temporal power spectral preserves the spatiotemporal gait features and is adaptable for a quadratic support vector machine classifier-based gait recognition across different views and appearances. The spatio temporal power spectral gait features are utilized for a quadratic support vector machine classifier for gait recognition aiming for person identification. For this purpose, the spatio temporal power spectral gait features are utilized for gait dynamics captured through sub-pixel motion estimation, and they are less affected by the subject’s appearance. The proposed work utilizes gait dynamics for gait feature extraction.

The gait features of the complete gait cycle, referred to as gait signature, can be used for person identification. The gait is a cycle pattern of human body locomotion that consists of two sequential phases: swing and stance.

Gait is the soft biometric trait recognizable from low-resolution surveillance videos, where the face and other hard biometrics are not even extractable. This study aimed to develop a vision-based gait recognition system for person identification.

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