Tracking and Matching Connected Components from 3D Video

Pires, David da Silva; César, Roberto M.; Vieira, Marcelo Bernardes; Velho, Luiz

doi:10.1109/sibgrapi.2005.49

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…In our case, we have implemented an algorithm from the Estimation Distribution Algorithms (EDAs) family, specifically the Univariate Marginal Distribution Algorithm (UMDA) (Mu¨hlenbein 1997). A Connected Components (CC) tracking algorithm (Silva 2005), which uses a nearest neighbor strategy to determine the blob-to-track assignment.…”

Section: Resultsmentioning

confidence: 99%

Fuzzy region assignment for visual tracking

et al. 2011

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In this work we propose a new approach based on fuzzy concepts and heuristic reasoning to deal with the visual data association problem in real time, considering the particular conditions of the visual data segmented from images, and the integration of higher-level information in the tracking process such as trajectory smoothness, consistency of information, and protection against predictable interactions such as overlap/occlusion, etc. The objects' features are estimated from the segmented images using a Bayesian formulation, and the regions assigned to update the tracks are computed through a fuzzy system to integrate all the information. The algorithm is scalable, requiring linear computing resources with respect to the complexity of scenarios, and shows competitive performance with respect to other classical methods in which the number of evaluated alternatives grows exponentially with the number of objects.

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Section: Resultsmentioning

confidence: 99%

Fuzzy region assignment for visual tracking

et al. 2011

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“…After validation, a strategy is needed to associate the measurements with the current targets. In addition to the Nearest Neighbour Filter, which selects the closest measurement, or affine techniques such as the Connect-Component approach (CC filter) (Silva et al, 2005), techniques such as Probabilistic Data Association Filter (PDAF) are available for the single target case. The underlying assumption of the PDAF is that for any given target only one measurement is valid, and the other measurements are modelled as random samples.…”

Section: Surveillance Video Systemsmentioning

confidence: 99%

“…Points which distance is below a threshold are considered to be connected. Then, the tracking procedure identifies which CC in frame (t 2 1) matches with the corresponding ones in the frame t (Silva et al, 2005). MS (Mean Shift Tracking) or Kernel Based Tracking: The core of this tracking system consists of mean shift iterations that find the target candidate that is the most similar to a given target model calculated in previous frames.…”

Section: Benchmarkmentioning

confidence: 99%

“…A particle filter samples some particles and subsequently, those particles are shifted to their respective local maximum of target searching space by mean shift (Zhang et al, 2006). CCMSPF (Connected Component tracking and MSPF resolver for collision): It uses a Connect Component tracking (Silva et al, 2005) in addition to a MSPF in case a collision is found it. MSFG (Mean Shift Tracking with FG mask using): The tracking system consists of a simple Mean Shift algorithm with an adjustable foreground mask (Li et al, 2004).…”

Section: Benchmarkmentioning

confidence: 99%

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Video tracking system optimization using evolution strategies

García

Pérez

Berlanga

et al. 2007

Int J Imaging Syst Tech

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A video-based tracking system for airport surveillance, composed by modules performing vision tasks at different levels, is adapted for operational conditions by means of Evolution Strategies (ES). An optimization procedure has been carried out considering different scenes composed of representative trajectories, supported by a global evaluation metric proposed to quantify the system performance. The generalization problem (the search of appropriate solutions for general situations, avoiding over-adaptation to particular conditions) is approached considering evaluation of ES-individuals over combinations of trajectories to build the fitness function. In this way, the optimization procedure covers sets of trajectories representing different types of problems. Besides, alternative operators for aggregating partial evaluations have been analysed. Results show how the optimization strategy provides a sensitive tuning of performance related to input parameters at different levels, and how the combination of different situations improves the generalization capability of the trained system. The global performance final system after optimization is also compared with representative algorithms in the state of the art of visual tracking.

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