Characteristics of video data for signal analysis

Sridharan, Patmaa; Raman, Shanmuganathan

doi:10.1109/icsigp.1996.566522

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…For effective retrieval, video data need to be stored with an appropriate data structure [3] and be analyzed based on not only temporal and spatial but also multiple video streams [4]. In other hands, it is important to determine where and how to process and store video data and its information because analyzing the video data and extracting information from the data are both dataintensive and compute-intensive jobs.…”

Section: A Video Bankmentioning

confidence: 99%

Towards Quality Aware Collaborative Video Analytic Cloud

Lee

Feng

Shi

et al. 2012

2012 IEEE Fifth International Conference on Cloud Computing

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As cloud diversifies into different application fields, understanding and characterizing the specific workloads and application requirements play important roles in the design of efficient cloud infrastructure and system software support. Video analytic is a rapidly advancing field and it is widely used in many application domains (i.e., health, medical care, surveillance, and defense). To support video analytic applications efficiently in cloud, one has to overcome many challenges such as lack of understanding of the relationship and tradeoff between analytic performance metrics and resource requirements. Furthermore, cloud computing has grown from the early model of resource sharing to data sharing and workflow sharing. To address the challenges and to leverage emerging trends, we propose and experiment with a domain specific cloud environment for video analytic applications. We design a cloud infrastructure framework for sharing video data, analytic software, and workflow. In addition, we create a video analytic quality aware resource plan model to guarantee users QoS and optimize usage of resources based on predictive knowledge of video analytic softwares performance metrics and a resource planning model that optimizes the overall analytic service quality under users constraints (i.e., time and cost). The predictive knowledge is represented as input and analytic software specific predictors. The experimental results show that the video analytic quality aware resource planning model can balance the tradeoff between analytic quality and resource requirements, and achieve optimal or near-optimal planning for video analytic workloads with constraints in a resource shared environment. Simulation studies show that resource planning results using ground truth and video analytic performance predictions are very similar, which indicates that our analytic quality/resource predictors are very accurate.

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Section: A Video Bankmentioning

confidence: 99%

Towards Quality Aware Collaborative Video Analytic Cloud

Lee

Feng

Shi

et al. 2012

2012 IEEE Fifth International Conference on Cloud Computing

View full text Add to dashboard Cite

show abstract

“…A five-level abstraction technique is employed for extracting categories from video data. The data structure is unique, as its meaning and organisation are based on the appealing characteristics of video Natural language interface to video database data, namely richness of information content, rapid context switching, and spontaneous reaction content (Sridharan and Raman 1996b).…”

Section: The Video Annotation Modelmentioning

confidence: 99%

“…Unlike conventional database items, video data cannot be easily represented by a fixed set of common properties or attributes. This is because video data represents real world events, and hence the contents are very diverse in nature (Sridharan and Raman 1996b). However, humans are adept in understanding the contents and deriving the attributes, but not necessarily in knowing what they are or how to express them precisely.…”

Section: Introductionmentioning

confidence: 99%

Natural language interface to video database

Gayatri

Raman

2001

Nat. Lang. Eng.

Self Cite

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In this paper, we discuss a natural language interface to a database of structured textual descriptions in the form of annotations of video objects. The interface maps the natural language query input on to the annotation structures. The language processing is done in three phases of expectations and implications from the input word, disambiguation of noun implications and slot-filling of prepositional expectations, and finally, disambiguation of verbal expectations. The system has been tested with different types of user inputs, including ill-formed sentences, and studied for erroneous inputs and for different types of portability issues.

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“…Massive amounts of video data are ubiquitously generated everyday from many different sources such as personal cameras and smart phones, traffic monitoring and video surveillance facilities, and many other video recording devices. Analyzing such complex, unstructured and voluminous data [47] would be extremely beneficial in real world (e.g., video surveillance). For instance, traffic monitoring videos can be analyzed by traffic authorities, urban planning officials, and some researchers [6] for learning urban traffic and pedestrian behavior.…”

Section: Introductionmentioning

confidence: 99%

VideoDP: A Flexible Platform for Video Analytics with Differential Privacy

Wang

Xie

Yuan

2020

Proceedings on Privacy Enhancing Technologies

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Massive amounts of videos are ubiquitously generated in personal devices and dedicated video recording facilities. Analyzing such data would be extremely beneficial in real world (e.g., urban traffic analysis). However, videos contain considerable sensitive information, such as human faces, identities and activities. Most of the existing video sanitization techniques simply obfuscate the video by detecting and blurring the region of interests (e.g., faces, vehicle plates, locations and timestamps). Unfortunately, privacy leakage in the blurred video cannot be effectively bounded, especially against unknown background knowledge. In this paper, to our best knowledge, we propose the first differentially private video analytics platform (VideoDP) which flexibly supports different video analyses with rigorous privacy guarantee. Given the input video, VideoDP randomly generates a utility-driven private video in which adding or removing any sensitive visual element (e.g., human, and object) does not significantly affect the output video. Then, different video analyses requested by untrusted video analysts can be flexibly performed over the sanitized video with differential privacy. Finally, we conduct experiments on real videos, and the experimental results demonstrate that VideoDP can generate accurate results for video analytics.

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Characteristics of video data for signal analysis

Cited by 4 publications

References 9 publications

Towards Quality Aware Collaborative Video Analytic Cloud

Towards Quality Aware Collaborative Video Analytic Cloud

Natural language interface to video database

VideoDP: A Flexible Platform for Video Analytics with Differential Privacy

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