Adaptive Synopsis of Non-Human Primates’ Surveillance Video Based on Behavior Classification

2016 ◽  
pp. 710-721
Author(s):  
Dongqi Cai ◽  
Fei Su ◽  
Zhicheng Zhao
Keyword(s):  
Surveillance Video

Multiprocessor transport surveillance video system based on "system on chip" technology

2020 ◽  
Author(s):  
Ш.С. Фахми ◽  
Н.В. Шаталова ◽  
В.В. Вислогузов ◽  
Е.В. Костикова
Keyword(s):  
System On Chip ◽  
Reference Points ◽  
Surveillance Video ◽  
Video Information ◽  
Chip Technology ◽  
Intelligent Video Surveillance System ◽  
Speed Up ◽  
Algorithmic Analysis ◽  
Computational Procedures ◽  
On Chip

В данной работе предлагаются математический аппарат и архитектура многопроцессорной транспортной системы на кристалле (МПТСнК). Выполнена программно-аппаратная реализация интеллектуальной системы видеонаблюдения на базе технологии «система на кристалле» и с использованием аппаратного ускорителя известного метода формирования опорных векторов. Архитектура включает в себя сложно-функциональные блоки анализа видеоинформации на базе параллельных алгоритмов нахождения опорных точек изображений и множества элементарных процессоров для выполнения сложных вычислительных процедур алгоритмов анализа с использованием средств проектирования на базе реконфигурируемой системы на кристалле, позволяющей оценить количество аппаратных ресурсов. Предлагаемая архитектура МПТСнК позволяет ускорить обработку и анализ видеоинформации при решении задач обнаружения и распознавания чрезвычайных ситуаций и подозрительных поведений. In this paper, we propose the mathematical apparatus and architecture of a multiprocessor transport system on a chip (MPTSoC). Software and hardware implementation of an intelligent video surveillance system based on the "system on chip" technology and using a hardware accelerator of the well-known method of forming reference vectors. The architecture includes complex functional blocks for analyzing video information based on parallel algorithms for finding image reference points and a set of elementary processors for performing complex computational procedures for algorithmic analysis. using design tools based on a reconfigurable system on chip that allows you to estimate the amount of hardware resources. The proposed MPTSoC architecture makes it possible to speed up the processing and analysis of video information when solving problems of detecting and recognizing emergencies and suspicious behaviors

Two-stream deep spatial-temporal auto-encoder for surveillance video abnormal event detection

2021 ◽  
Vol 439 ◽  
pp. 256-270
Author(s):  
Tong Li ◽  
Xinyue Chen ◽  
Fushun Zhu ◽  
Zhengyu Zhang ◽  
Hua Yan
Keyword(s):  
Event Detection ◽  
Surveillance Video ◽  
Abnormal Event Detection

A Case Study Demonstrating the Utility of Surveillance Video in Assessing Gait Dynamics and Behavior During a Slip and Fall

2020 ◽  
Vol 64 (1) ◽  
pp. 550-554
Author(s):  
Leah S. Hartman ◽  
Stephanie A. Whetsel Borzendowski ◽  
Alan O. Campbell
Keyword(s):  
Human Factors ◽  
Gait Pattern ◽  
Personal Injury ◽  
Objective Data ◽  
Surveillance Video ◽  
Gait Dynamics ◽  
Fall Incident ◽  
The Individual ◽  
And Behavior

As the use of surveillance video at commercial properties becomes more prevalent, it is more likely an incident involving a personal injury will be captured on film. This provides a unique opportunity for Human Factors practitioners involved in forensic investigations to analyze the behavior of the individual prior to, during, and after the event in question. It also provides an opportunity to gather unique and objective data. The present work describes a case study of a slip and fall where surveillance video and onsite measurements were combined and analyzed to quantify a plaintiff’s gait pattern. Using this type of analysis, we were able to determine that the plaintiff was likely aware that the floor was slippery and adjusted her gait and behavior prior to the slip and fall incident.

scholarly journals Natural Language Description of Videos for Smart Surveillance

2021 ◽  
Vol 11 (9) ◽  
pp. 3730
Author(s):  
Aniqa Dilawari ◽  
Muhammad Usman Ghani Khan ◽  
Yasser D. Al-Otaibi ◽  
Zahoor-ur Rehman ◽  
Atta-ur Rahman ◽  
...  
Keyword(s):  
Natural Language ◽  
Feature Recognition ◽  
Scene Recognition ◽  
Video Data ◽  
Surveillance Video ◽  
Video Footage ◽  
Parallel Pipeline ◽  
September 11 Attacks ◽  
Description Framework ◽  
High Level

After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions.

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