894 research outputs found
A Real-Time Feature Indexing System on Live Video Streams
Most of the existing video storage systems rely on offline processing to support the feature-based indexing on video streams. The feature-based indexing technique provides an effec- tive way for users to search video content through visual features, such as object categories (e.g., cars and persons). However, due to the reliance on offline processing, video streams along with their captured features cannot be searchable immediately after video streams are recorded. According to our investigation, buffering and storing live video steams are more time-consuming than the YOLO v3 object detector. Such observation motivates us to propose a real-time feature indexing (RTFI) system to enable instantaneous feature-based indexing on live video streams after video streams are captured and processed through object detectors. RTFI achieves its real-time goal via incorporating the novel design of metadata structure and data placement, the capability of modern object detector (i.e., YOLO v3), and the deduplication techniques to avoid storing repetitive video content. Notably, RTFI is the first system design for realizing real-time feature-based indexing on live video streams. RTFI is implemented on a Linux server and can improve the system throughput by upto 10.60x, compared with the base system without the proposed design. In addition, RTFI is able to make the video content searchable within 20 milliseconds for 10 live video streams after the video content is received by the proposed system, excluding the network transfer latency
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet
everyday has led to many commercial video search engines, which mainly rely on
text metadata for search. However, metadata is often lacking for user-generated
videos, thus these videos are unsearchable by current search engines.
Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity
problem by directly analyzing the visual and audio streams of each video. CBVR
encompasses multiple research topics, including low-level feature design,
feature fusion, semantic detector training and video search/reranking. We
present novel strategies in these topics to enhance CBVR in both accuracy and
speed under different query inputs, including pure textual queries and query by
video examples. Our proposed strategies have been incorporated into our
submission for the TRECVID 2014 Multimedia Event Detection evaluation, where
our system outperformed other submissions in both text queries and video
example queries, thus demonstrating the effectiveness of our proposed
approaches
Enhancing camera surveillance using computer vision: a research note
- The growth of police operated surveillance cameras has
out-paced the ability of humans to monitor them effectively. Computer vision is
a possible solution. An ongoing research project on the application of computer
vision within a municipal police department is described. The paper aims to
discuss these issues.
- Following the demystification of
computer vision technology, its potential for police agencies is developed
within a focus on computer vision as a solution for two common surveillance
camera tasks (live monitoring of multiple surveillance cameras and summarizing
archived video files). Three unaddressed research questions (can specialized
computer vision applications for law enforcement be developed at this time, how
will computer vision be utilized within existing public safety camera
monitoring rooms, and what are the system-wide impacts of a computer vision
capability on local criminal justice systems) are considered.
- Despite computer vision becoming accessible to law
enforcement agencies the impact of computer vision has not been discussed or
adequately researched. There is little knowledge of computer vision or its
potential in the field.
- This paper introduces and discusses computer
vision from a law enforcement perspective and will be valuable to police
personnel tasked with monitoring large camera networks and considering computer
vision as a system upgrade
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
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