Law enforcement officials heavily depend on Forensic Video Analytic (FVA)
Software in their evidence extraction process. However present-day FVA software
are complex, time consuming, equipment dependent and expensive. Developing
countries struggle to gain access to this gateway to a secure haven. The term
forensic pertains the application of scientific methods to the investigation of
crime through post-processing, whereas surveillance is the close monitoring of
real-time feeds.
The principle objective of this Final Year Project was to develop an
efficient and effective FVA Software, addressing the shortcomings through a
stringent and systematic review of scholarly research papers, online databases
and legal documentation. The scope spans multiple object detection, multiple
object tracking, anomaly detection, activity recognition, tampering detection,
general and specific image enhancement and video synopsis.
Methods employed include many machine learning techniques, GPU acceleration
and efficient, integrated architecture development both for real-time and
postprocessing. For this CNN, GMM, multithreading and OpenCV C++ coding were
used. The implications of the proposed methodology would rapidly speed up the
FVA process especially through the novel video synopsis research arena. This
project has resulted in three research outcomes Moving Object Based Collision
Free Video Synopsis, Forensic and Surveillance Analytic Tool Architecture and
Tampering Detection Inter-Frame Forgery.
The results include forensic and surveillance panel outcomes with emphasis on
video synopsis and Sri Lankan context. Principal conclusions include the
optimization and efficient algorithm integration to overcome limitations in
processing power, memory and compromise between real-time performance and
accuracy.

通过对学术研究论文、在线数据库和法律文件的严格和系统的审查，开发了一种高效且有效的法庭图像分析软件，涵盖了多个对象检测、活动识别、视频概览等领域，以解决目前在开发中国家中使用复杂、费时、依赖设备并且昂贵的现有软件的问题。

取证视频分析软件

Forensic Video Analytic Software

Video synopsis, summarizing a video to generate a shorter video by exploiting
the spatial and temporal redundancies, is important for surveillance and
archiving. Existing trajectory-based video synopsis algorithms will not able to
work in real time, because of the complexity due to the number of object tubes
that need to be included in the complex energy minimization algorithm. We
propose a real-time algorithm by using a method that incrementally stitches
each frame of the synopsis by extracting object frames from the user specified
number of tubes in the buffer in contrast to global energy-minimization based
systems. This also gives flexibility to the user to set the threshold of
maximum number of objects in the synopsis video according his or her tracking
ability and creates collision-free summarized videos which are visually
pleasing. Experiments with six common test videos, indoors and outdoors with
many moving objects, show that the proposed video synopsis algorithm produces
better frame reduction rates than existing approaches.

实时视频摘要算法通过从用户指定数量的管道中提取对象帧来逐帧拼接摘要视频，与全局能量最小化系统相比具有更大的灵活性，该算法可以根据用户的跟踪能力设置对象数量阈值，生成无碰撞、视觉效果良好的摘要视频，实验证明该算法在减帧率方面优于现有方法。