[논문]Adaptive Bayesian Object Tracking with Histograms of Dense Local Image Descriptors

Kim, Minyoung

doi:10.5391/ijfis.2016.16.2.104

Adaptive Bayesian Object Tracking with Histograms of Dense Local Image Descriptors 원문보기

International journal of fuzzy logic and intelligent systems : IJFIS, v.16 no.2, 2016년, pp.104 - 110

Kim, Minyoung (Department of Electronics & IT Media Engineering, Seoul National University of Science & Technology)

Abstract ▼ AI-Helper

Dense local image descriptors like SIFT are fruitful for capturing salient information about image, shown to be successful in various image-related tasks when formed in bag-of-words representation (i.e., histograms). In this paper we consider to utilize these dense local descriptors in the object tracking problem. A notable aspect of our tracker is that instead of adopting a point estimate for the target model, we account for uncertainty in data noise and model incompleteness by maintaining a distribution over plausible candidate models within the Bayesian framework. The target model is also updated adaptively by the principled Bayesian posterior inference, which admits a closed form within our Dirichlet prior modeling. With empirical evaluations on some video datasets, the proposed method is shown to yield more accurate tracking than baseline histogram-based trackers with the same types of features, often being superior to the appearance-based (visual) trackers.

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Our second contribution is that we account for uncertainty in the target appearance model by maintaining a distribution over plausible candidate models within the Bayesian framework. Instead of having a single point estimate for the target model, our approach performs a model averaging for prediction, yielding a tracker more robust to noise in observation (e.g., partial occlusion).
, directions of maximal intensity changes). Motivated by this, in this paper we propose a histogram of the SIFT codewords as a target appearance model. The SIFT [11] descriptors are robust in illumination and viewpoint changes, successful in various tasks including image classification, matching, and annotation [12, 13].

참고문헌 (18)

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Adaptive Bayesian Object Tracking with Histograms of Dense Local Image Descriptors 원문보기

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Adaptive Bayesian Object Tracking with Histograms of Dense Local Image Descriptors 원문보기

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