Segmentation and tracking of video objects for a content-based video indexing context

Maziere, M.; Chassaing, F.; Garrido, L.; Salembier, Philippe

doi:10.1109/icme.2000.871574

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…While the advent of big data machine analytics (Sect. 4.4), due to high-performance computers and cloud computing, is new, one of the first context tracking papers uses video results to index context [27].…”

Section: Background On Contextual Tracking Methodsmentioning

confidence: 99%

Contextual Tracking Approaches in Information Fusion

Blasch

Yang²,

García

et al. 2016

Context-Enhanced Information Fusion

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Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this chapter, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion designs to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application

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Section: Background On Contextual Tracking Methodsmentioning

confidence: 99%

Contextual Tracking Approaches in Information Fusion

Blasch

Yang²,

García

et al. 2016

Context-Enhanced Information Fusion

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show abstract

“…Temporal Difference Method [11] is based on the detection of the differences of the sequential video frames. Another method is the active contour model, which is an edgebased segmentation method [12,13]. This method is based on the framing of the edges of an object with a closed frame by the energy function within the object area [14].…”

Section: Related Wokmentioning

confidence: 99%

New Automatic Target Recognition Approach based on Hough Transform and Mutual Information

Bahy¹

2018

IJIGSP

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This paper presents a new automatic target recognition approach based on Hough transform and mutual information. The Hough transform groups the extracted edge points in edged images to an appropriate set of lines which helps in features extraction and matching processes in both of target and stored database images. This gives an initial indication about realization and recognition between target image and its corresponding database image. Mutual information is used to emphasize the recognition of the target image and its verification with its corresponding database image. The proposed recognition approach passed through five stages which are: edge detection by Sobel edge detector, thinning as a morphological operation, Hough transformation, matching process and finally measuring the mutual information between target and the available database images. The experimental results proved that, the target recognition is realized and gives more accurate and successful recognition rate than other recent recognition techniques which are based on stable edge weighted HOG.

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“…It is the environmental constraints that affords context-enhanced target tracking (DFIG Level 1), wherein improved performance is reported. While the advent of big data machine analytics (Section 4), due to high-performance computers and cloud computing, is new, one of the first context tracking papers uses video results to index context [25].…”

Section: Contextual Tracking Methods (Review)mentioning

confidence: 99%

Overview of contextual tracking approaches in information fusion

et al. 2013

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Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of: technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this paper, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.

show abstract

Segmentation and tracking of video objects for a content-based video indexing context

Cited by 6 publications

References 7 publications

Contextual Tracking Approaches in Information Fusion

Contextual Tracking Approaches in Information Fusion

New Automatic Target Recognition Approach based on Hough Transform and Mutual Information

Overview of contextual tracking approaches in information fusion

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