Adaptive camera assignment and hand-off algorithm in multiple active camera surveillance system

Lee, Chao‐Yang; Lin, Shou-Jen; Lee, Chen-Wei; Yang, Chu-Sing

doi:10.1109/icmlc.2010.5580739

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Camera handoff was developed to manage camera switching in a manner conducive to seamless target tracking. (12,13) However, almost all handoff techniques rely on robust camera trackers. State-ofthe-art techniques for evaluating camera handoff performance use either annotated videos or simulated data and operate in conjunction with a tracker.…”

Section: Literature Review and Methodologymentioning

confidence: 99%

Camera Handoff for Multicamera Multiobject Tracking

Wang¹,

Sheu²,

Lai³

2022

Sensors and Materials

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In recent years, the unmanned economy has begun to develop rapidly worldwide. Unmanned shops use technology to replace human personnel and increase efficiency. This technology has increased the demand for accurate and efficient camera surveillance systems. Camera handoff is a crucial step in the tasks of continuous target tracking and the maintenance of consistent crosscamera target marking in a multicamera surveillance system. In this study, we proposed a method of indoor multicamera handoff. To ensure continuous target tracking, the minimum number of required frames was maintained and at least one camera tracked each target. We employed the background subtraction approach to detect the target. Next, we used three trackability measures to evaluate the tracking object and trigger camera handoff accordingly, selecting the optimal camera for target tracking. The three measures considered were the resolution, occlusion, and distance to the edge of the camera's field of view. When one of these reached a preset threshold with an increasing trend, the system triggered the camera handoff.

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Section: Literature Review and Methodologymentioning

confidence: 99%

Camera Handoff for Multicamera Multiobject Tracking

Wang¹,

Sheu²,

Lai³

2022

Sensors and Materials

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“…The third column shows selected constants in binary forms. The last column represents error probability calculated by (6).…”

Section: Optimization Of Constant Multiplicationmentioning

confidence: 99%

Hardware architecture dedicated for arithmetic mean filtration implemented in FPGA

Malík

2013

2013 8th International Conference on Computer Engineering &Amp; Systems (ICCES)

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An FPGA-based hardware architecture for arithmetic mean filtration optimized with 49-pixel square neighborhood is proposed. The arithmetic mean formula is optimized and transformed into the new formula that introduces the computational cyclic sequence which results in multiplication-less process with only 9 additions necessary for each pixel. The external memory is used to save partial results but the memory requirement has been optimized so the requirement is the same as for the input data. This proposed architecture is oriented to security tracking applications; however, it can be used in any image processing applications that use arithmetic mean filtering. It is resolution and frame rate independent and suitable for all high resolution and multiple camera systems. FPGA optimization made it also suitable for FPGA-based reconfigurable systems and computing.

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