Active models for tracking moving objects

Jang, Daesik; Choi, Hyung-Il

doi:10.1016/s0031-3203(99)00100-4

Cited by 64 publications

(31 citation statements)

References 15 publications

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“…During the iterative search process for human objects, in order to avoid exhaustive search of the new target location to reduce the cost of computation, the most widely used tracking methods include Kalman filtering (KF) [4][5][6][7], Particle filtering (PF) [8][9][10][11], and kernel-based tracking (KT) [12][13][14][15][16]. KF expresses a target movement as a dynamic process over the temporal frames and uses the previous target state to predict the next location (and possible size), and then uses the current observation to update the target location.…”

Section: Human Tracking Within a Cameramentioning

confidence: 99%

“…Jang et al [4] propose active models-based KF tracking algorithm to handle inter-frame changes of non-rigid human objects such as illumination changes and shape deformation. This method applies the framework of energy minimization to active models which characterizes structural and regional features of a human object such as edge, shape, color as well as texture, and hence, adapts dynamically the changes of non-rigid human objects in the consecutive video frames.…”

Section: Kfmentioning

confidence: 99%

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Human tracking over camera networks: a review

Hou

Wan

Hwang

et al. 2017

EURASIP J. Adv. Signal Process.

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In recent years, automated human tracking over camera networks is getting essential for video surveillance. The tasks of tracking human over camera networks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, ranging from security surveillance to retail and health care. This review paper surveys the most widely used techniques and recent advances for human tracking over camera networks. Two important functional modules for the human tracking over camera networks are addressed, including human tracking within a camera and human tracking across nonoverlapping cameras. The core techniques of human tracking within a camera are discussed based on two aspects, i.e., generative trackers and discriminative trackers. The core techniques of human tracking across non-overlapping cameras are then discussed based on the aspects of human re-identification, camera-link model-based tracking and graph model-based tracking. Our survey aims to address existing problems, challenges, and future research directions based on the analyses of the current progress made toward human tracking techniques over camera networks.

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Section: Human Tracking Within a Cameramentioning

confidence: 99%

Section: Kfmentioning

confidence: 99%

Human tracking over camera networks: a review

Hou

Wan

Hwang

et al. 2017

EURASIP J. Adv. Signal Process.

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“…Lepetit and Fua [10] have described the general principles of feature detection, tracking and 3-D reconstruction, and Oliensis [11] gave an earlier detailed critique of the comparative strengths and weaknesses of several, well-documented approaches to SFM. The majority of existing methods for feature tracking use frame-toframe prediction models, based for example on Kalman filter [12,13], particle filtering [14], and optimizationbased approaches [15][16][17]. One of the numerous examples is the MonoSLAM system developed by Davison et al [18], who utilized a probabilistic feature-based map that represents a snapshot of the current estimates of the state of the camera and the overall feature points.…”

Section: Introductionmentioning

confidence: 99%

Efficient tracking and ego-motion recovery using gait analysis

2009

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a b s t r a c tWe present a strategy based on human gait to achieve efficient tracking, recovery of egomotion and 3-D reconstruction from an image sequence acquired by a single camera attached to a pedestrian. In the first phase, the parameters of the human gait are established by a classical frame-by-frame analysis, using an generalized least squares (GLS) technique. The gait model is non-linear, represented by a truncated Fourier series. In the second phase, this gait model is employed within a ''predict-correct'' framework using a maximum a posteriori, expectation-maximization (MAP-EM) strategy to obtain robust estimates of the ego-motion and scene structure, while continuously refining the gait model. Experiments on synthetic and real image sequences show that the use of the gait model results in more efficient tracking. This is demonstrated by improved matching and retention of features, and a reduction in execution time, when processing video sequences.

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“…Consequently, trajectories obtained from different cameras are fused in the same ground plane. Occlusion handling is a complex task that has been thoroughly investigated [1], which can be classified in four areas: region based tracking [2], active contour-based tracking [3], feature-based tracking [4], and model-based tracking [5]. Existing approaches include Extended Kalman Filters (EKF) was used for occlusion analysis [6].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Fusion for Multiple Camera Tracking

Amato

Haj

Mozerov

et al. 2007

Advances in Soft Computing

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Summary. In this paper we present a robust and efficient method to overcome the negative effects of occlusion in the tracking process of multiple agents. The proposed approach is based on the matching of multiple trajectories from multiple views using spatial and temporal information. These trajectories are represented as consecutive points of a joint ground plane in the world coordinate system that belong to the same tracked agent. We introduce an integral distance between compared trajectories, which allows us to avoid mismatches, due to the possible measurement outliers in one frame. The proposed method can also be considered as an interpolation algorithm of a disconnected trajectory during the time of occlusion. This technique solves one of the most difficult problems of occlusion handling, which is a matching of two unconnected parts of the same trajectory.

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Active models for tracking moving objects

Cited by 64 publications

References 15 publications

Human tracking over camera networks: a review

Human tracking over camera networks: a review

Efficient tracking and ego-motion recovery using gait analysis

Trajectory Fusion for Multiple Camera Tracking

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