Fast object tracking using adaptive block matching

Hariharakrishnan, K.; Schonfeld, Dan

doi:10.1109/tmm.2005.854437

Cited by 104 publications

(56 citation statements)

References 18 publications

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“…Walther et al [69] developed an automatic machine vision system for animal detection and tracking by using high-resolution video equipment on board of the ROV (ocean-going remotely operated vehicles-ROV). Hariharakrishnan and Schonfeld [28] proposed a tracking system based on the prediction of object contour by analyzing motion vector information.…”

Section: Object Detection and Tracking In Underwater Scenesmentioning

confidence: 99%

Understanding fish behavior during typhoon events in real-life underwater environments

Spampinato

Palazzo

Boom

et al. 2012

Multimed Tools Appl

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The study of fish populations in their own natural environment is a task that has usually been tackled in invasive ways which inevitably influenced the behavior of the fish under observation. Recent projects involving the installation of permanent underwater cameras (e.g. the Fish4Knowledge (F4K) project, for the observation of Taiwan's coral reefs) allow to gather huge quantities of video data, without interfering with the observed environment, but at the same time require the development of automatic processing tools, since manual analysis would be impractical for such amounts of videos. Event detection is one of the most interesting This research was funded by European Commission FP7 grant 257024, for the Fish4Knowledge project (www.fish4knowledge.eu). Appl (2014) 70:199-236 aspects from the biologists' point of view, since it allows the analysis of fish activity during particular events, such as typhoons. In order to achieve this goal, in this paper we present an automatic video analysis approach for fish behavior understanding during typhoon events. The first step of the proposed system, therefore, involves the detection of "typhoon" events and it is based on video texture analysis and on classification by means of Support Vector Machines (SVM). As part of our behavior understanding efforts, trajectory extraction and clustering have been performed to study the differences in behavior when disruptive events happen. The integration of event detection with fish behavior understanding surpasses the idea of simply detecting events by low-level features analysis, as it supports the full semantic comprehension of interesting events.

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Section: Object Detection and Tracking In Underwater Scenesmentioning

confidence: 99%

Understanding fish behavior during typhoon events in real-life underwater environments

Spampinato

Palazzo

Boom

et al. 2012

Multimed Tools Appl

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“…Person re-identification (re-id), namely, seeking occurrences of a query person (probe) from person candidates (gallery), is a hot-spot and challenging topic of intelligent video surveillance [1,2], which also underpins many crucial multimedia applications, such as person retrieval [3,4], long-term pedestrian tracking [5,6], and cross-view action analysis [7]. The main challenge of re-id can be concluded as intrapersonal visual variations across multicamera views even larger than interpersonal ones, due to the significant changes in viewpoints, illuminations, body poses, and background clutters (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Co-Metric Learning for Person Re-Identification

Leng

2018

Advances in Multimedia

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Person re-identification, aiming to identify the same pedestrian images across disjoint camera views, is a key technique of intelligent video surveillance. Although existing methods have developed both theories and experimental results, most of effective ones pertain to fully supervised training styles, which suffer the small sample size (SSS) problem a lot, especially in label-insufficient practical applications. To bridge SSS problem and learning model with small labels, a novel semisupervised co-metric learning framework is proposed to learn a discriminative Mahalanobis-like distance matrix for label-insufficient person re-identification. Different from typical co-training task that contains multiview data originally, single-view person images are firstly decomposed into pseudo two views, and then metric learning models are produced and jointly updated based on both pseudo-labels and references iteratively. Experiments carried out on three representative person re-identification datasets show that the proposed method performs better than state of the art and possesses low label sensitivity.

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“…Various tracking methods have been proposed and improved, from the simple and rigid object tracking with static camera, to the complex and non-rigid object tracking with moving camera [5]. These methods are categorized into five groups [6,7] namely, region-based tracking [8], feature-based tracking [9], mesh-based tracking [10,11], model-based tracking [12], and active contour models (ACM)-based tracking [13].…”

Section: Introductionmentioning

confidence: 99%

Balloon energy based on parametric active contour and directional Walsh–Hadamard transform and its application in tracking of texture object in texture background

Tahvilian

Moallem

Monadjemi

2012

EURASIP J. Adv. Signal Process.

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One of the popular approaches in object boundary detecting and tracking is active contour models (ACM). This article presents a new balloon energy in parametric active contour for tracking a texture object in texture background. In this proposed method, by adding the balloon energy to the energy function of the parametric ACM, a precise detection and tracking of texture target in texture background has been elaborated. In this method, texture feature of contour and object points have been calculated using directional Walsh-Hadamard transform, which is a modified version of the Walsh-Hadamard. Then, by comparing the texture feature of contour points with texture feature of the target object, movement direction of the balloon has been determined, whereupon contour curves are expanded or shrunk in order to adapt to the target boundaries. The tracking process is iterated to the last frames. The comparison between our method and the active contour method based on the moment demonstrates that our method is more effective in tracking object boundary edges used for video streams with a changing background. Consequently, the tracking precision of our method is higher; in addition, it converges more rapidly due to it slower complexity.

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Fast object tracking using adaptive block matching

Cited by 104 publications

References 18 publications

Understanding fish behavior during typhoon events in real-life underwater environments

Understanding fish behavior during typhoon events in real-life underwater environments

Co-Metric Learning for Person Re-Identification

Balloon energy based on parametric active contour and directional Walsh–Hadamard transform and its application in tracking of texture object in texture background

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