Online and Offline Object Detection in Video For Forensic Analysis
AbstractAbstract— In the era of digitalization, many companies, agencies and even homes in the world have installed the closed- circuit television (CCTV) in their premise as the surveillance and security purposes. The footage videos from CCTV have been used as the sources of the evidence in the criminal investigation. In video forensic analysis, the footage of CCTV with the potential subject or object is extracted out from the CCTV recordings for the analysis. Law enforcement agencies depend on Forensic Video Analysis (FVA) software in their process of evidence extraction. However, the majority of the current software is expensive and complex but it also needs time to analyze. Object detection is the main key to digital forensics of evidence from CCTV videos. The process of digital forensics can be difficult and it may require a high level of video analysis of lengthy contents that acquire from CCTV cameras. Every image and video collected from CCTV is counted as evidence in order to create visual documentation of the crime scene. The main purpose of this paper is to assists the forensic investigation by developing an object detection system that able to speed up the process of the extraction of evidence and able to detect objects without human involvement or any external control. To assist the video-based forensic analysis, a convolutional neural networks-based algorithm object detection known as YOLO: Real-Time Object Detection is proposed that can detect and identify potential subjects and objects from CCTV footages. This proposed solution able to extract the detected image with a timestamp in online video (such as live CCTV, live webcam and live streaming video) and offline video (such as CCTV footage and recording video). These implements include deep learning techniques, Graphics Processing Unit (GPU) computing and efficient, integrated architecture development both for real-time and post-processing.
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