7,442 research outputs found
Forensic Video Analytic Software
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.Comment: The Forensic Video Analytic Software demo video is available
https://www.youtube.com/watch?v=vsZlYKQxSk
Moving Object Based Collision-Free Video Synopsis
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.Comment: The summarized output videos are available at
https://anton-jeran.github.io/M2SYN
A comprehensive survey of multi-view video summarization
[EN] There has been an exponential growth in the amount of visual data on a daily basis acquired from single or multi-view surveillance camera networks. This massive amount of data requires efficient mechanisms such as video summarization to ensure that only significant data are reported and the redundancy is reduced. Multi-view video summarization (MVS) is a less redundant and more concise way of providing information from the video content of all the cameras in the form of either keyframes or video segments. This paper presents an overview of the existing strategies proposed for MVS, including their advantages and drawbacks. Our survey covers the genericsteps in MVS, such as the pre-processing of video data, feature extraction, and post-processing followed by summary generation. We also describe the datasets that are available for the evaluation of MVS. Finally, we examine the major current issues related to MVS and put forward the recommendations for future research(1). (C) 2020 Elsevier Ltd. All rights reserved.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2B5B01070067)Hussain, T.; Muhammad, K.; Ding, W.; Lloret, J.; Baik, SW.; De Albuquerque, VHC. (2021). A comprehensive survey of multi-view video summarization. Pattern Recognition. 109:1-15. https://doi.org/10.1016/j.patcog.2020.10756711510
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