Temporal Mapping of Surveillance Video for Indexing and Summarization

This work converts the surveillance video to a temporal domain image called temporal profile that is scrollable and scalable for quick searching of long surveillance video by human operators. Such a profile is sampled with linear pixel lines located at critical locations in the video frames. It has precise time stamp on the target passing events through those locations in the field of view, shows target shapes for identification, and facilitates the target search in long videos. In this paper, we first study the projection and shape properties of dynamic scenes in the temporal profile so as to set sampling lines. Then, we design methods to capture target motion and preserve target shapes for target recognition in the temporal profile. It also provides the uniformed resolution of large crowds passing through so that it is powerful in target counting and flow measuring. We also align multiple sampling lines to visualize the spatial information missed in a single line temporal profile. Finally, we achieve real time adaptive background removal and robust target extraction to ensure long-term surveillance. Compared to the original video or the shortened video, this temporal profile reduced data by one dimension while keeping the majority of information for further video investigation. As an intermediate indexing image, the profile image can be transmitted via network much faster than video for online video searching task by multiple operators. Because the temporal profile can abstract passing targets with efficient computation, an even more compact digest of the surveillance video can be created

Changing Higher Education Learning with Web 2.0 and Open Education Citation, Annotation, and Thematic Coding Appendices

Appendices of citations, annotations and themes for research conducted on four websites: Delicious, Wikipedia, YouTube, and Facebook

Exploiting intra-warp address monotonicity for fast memory coalescing in GPUs

Graphics Processing Units (GPUs) are growing increasingly popular as general purpose compute accelerators. GPUs are best suited for applications which have abundant data parallelism wherein the computation expressed as a single thread can be applied over a large set of data items. One key constraint that affects application performance on GPUs is that the underlying hardware is single-instruction, multiple data (SIMD) hardware which requires parallel instructions from the multiple threads to execute in a lock-step manner. The benefits of lock-step execution can be seriously degraded if the threads diverge (because of memory or branches). Specifically in the case of memory, the addresses from each thread in a SIMD wavefront/warp must be coalesced to enable parallel memory access to minimize divergence. ^ The general problem of coalescing assumes arbitrary address distribution which can be slow. This thesis aims to exploit intra-warp address monotonicity (as measured in a recent study by Holic) to achieve fast memory coalescing. Holic\u27s study reveals the intra-warp addresses are monotonically increasing or decreasing in the common case. The key contributions of this thesis are twofold. First, I design novel hardware coalescing mechanisms to achieve fast-coalescing and quantify the area/delay of my coalescing designs. Second, I quantify the impact of fast-coalescing on overall GPU performance for a suite of GPU benchmarks

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