Abstract
Studies on abnormal behavior based on deep learning as a processing platform increase. Deep learning, specifically the convolutional neural network (CNN), is known for learning the features directly from the raw image. In return, CNN requires a high-performance hardware platform to accommodate its computational cost like AlexNet and VGG-16 with 62 million and 138 million parameters, respectively. Hence in this study, four CNN samplings with different architectures in detecting abnormal behavior at the gate of residential units are evaluated and validated. The forensic postures, with some other collected data, are used for the preliminary step in constructing the criminal case database. High accuracy up to 97% is obtained from the trained CNN samplings with 80% to 97% recognition rate achieved during the offline testing and 70% to 90% recognition rate recorded during the real-time testing. Results showed that the developed CNN samplings owned good performance and can be utilized in detecting and recognizing the normal and abnormal behavior at the gate of residential units.
Highlights
There is an increase in the usage of closed-circuit television (CCTV) in residential units as a consequence of the upbringing awareness of sheltered zone [1]–[3]
Based on previous work and findings by [14] that detected the criminal behavior using convolutional neural network (CNN) as motivation in this work, we aim to investigate the forensic postures on anomalous human behavior at the gate of residential units as the database
Forensic postures are used, and are referred to as the postures defined by the Royal Malaysia Police (RMP)
Summary
There is an increase in the usage of closed-circuit television (CCTV) in residential units as a consequence of the upbringing awareness of sheltered zone [1]–[3] This monotonous observation can cause fatigue and distraction, leading to negligence and being overlooked as the surveillance process is underway [4]. Gait is considered competent biometrics, suitable for forensic intelligent surveillance systems This is because gait as biometric has the potential for farther distance recognition, can be possessed without the perpetrators' consent and awareness, and can be perceived at a low-resolution camera. Combining these technologies, image recognition, CNN, and gait biometric brings us a little closer to developing a forensic intelligent surveillance system. The lack of data on criminal behavior in public databases leads to the problem of developing and designing adaptability features of forensic gait for recognition and detection
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