A Real-Time Object Tracking System Using a Particle Filter

Cho, Jung Uk; Jin, Seung Hun; Pham, Xuan Dai; Jeon, Jae Wook; Byun, Jong Eun; Kang, Hoon

doi:10.1109/iros.2006.282066

Cited by 64 publications

(34 citation statements)

References 14 publications

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“…In [114], for tracking moving objects in real-time without delay and loss of image sequences, a particle filter algorithm was implemented specifically for an electronic circuit for the goal of object tracking. This circuit was designed by VHDL (VHSIC Hardware Description Language) and implemented in an FPGA (Field Programmable Gate Array).…”

Section: Real Time Aspectsmentioning

confidence: 99%

New trends on moving object detection in video images captured by a moving camera: A survey

Yazdi

Bouwmans

2018

Computer Science Review

195

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This paper presents a survey on the latest methods of moving object detection in video sequences captured by a moving camera. Although many researches and excellent works have reviewed the methods of object detection and background subtraction for a fixed camera, there is no survey which presents a complete review of the existing different methods in the case of moving camera. Most methods in this field can be classified into four categories: modelling based background subtraction, trajectory classification, low rank and sparse matrix decomposition, and object tracking. We discuss in details each category and present the main methods which proposed improvements in the general concept of the techniques. We also present challenges and main concerns in this field as well as performance metrics and some benchmark databases available to evaluate the performance of different moving object detection algorithms.

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Section: Real Time Aspectsmentioning

confidence: 99%

New trends on moving object detection in video images captured by a moving camera: A survey

Yazdi

Bouwmans

2018

Computer Science Review

195

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“…Given a ROI pattern at frame , the input to the prediction filter is the previous state and the output is a number of predicted states defining the possible new locations and sizes of the face at . A particle filter is used for predicting the new states during tracking [12]. Adaptive appearance modeling inside the tracker generates TFMs for newly initiated tracks and updates the models for existing tracks.…”

Section: A Face Trackingmentioning

confidence: 99%

Video Face Recognition From A Single Still Image Using an Adaptive Appearance Model Tracker

Dewan

Granger

Sabourin

et al. 2015

2015 IEEE Symposium Series on Computational Intelligence

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Systems for still-to-video face recognition (FR) are typically used to detect target individuals in watch-list screening applications. These surveillance applications are challenging because the appearance of faces changes according to capture conditions, and very few reference stills are available a priori for enrollment. To improve performance, an adaptive appearance model tracker (AAMT) is proposed for on-line learning of a track-face-model linked to each individual appearing in the scene. Meanwhile, these models are matched over successive frames against stored gallery-face-models, extracted from reference still images of each target individual (enrolled to the system) for robust spatiotemporal FR. In addition, compared to the gallery-face-models produced by selfupdating FR systems, the track-face-models (produced by the AAMT-FR system) are updated from facial captures that are more reliably selected, and can incorporate greater intra-class variations from the operational environment. Track-facemodels allow selecting facial captures for modeling more reliably than self-updating FR systems, and can incorporate a greater diversity of intra-class variation from the operational environment. Performance of the proposed approach is compared with several state-of-the-art FR systems on videos from the Chokepoint dataset when a single reference template per target individual is stored in the gallery. Experimental results show that the proposed system can achieve a significantly higher level of FR performance, especially when the diverse facial appearances captured through AAMT correspond to that of reference stills.

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“…The previous related study, in the field of object tracking [5,20,21], used 64, 50, and 100 particles, respectively. In our previous study [22,23], we use 100 particles.…”

Section: Pf For Trackingmentioning

confidence: 99%

Proposed hardware architectures of particle filter for object tracking

El-Halym

Mahmoud

Habib

2012

EURASIP J. Adv. Signal Process.

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In this article, efficient hardware architectures for particle filter (PF) are presented. We propose three different architectures for Sequential Importance Resampling Filter (SIRF) implementation. The first architecture is a two-step sequential PF machine, where particle sampling, weight, and output calculations are carried out in parallel during the first step followed by sequential resampling in the second step. For the weight computation step, a piecewise linear function is used instead of the classical exponential function. This decreases the complexity of the architecture without degrading the results. The second architecture speeds up the resampling step via a parallel, rather than a serial, architecture. This second architecture targets a balance between hardware resources and the speed of operation. The third architecture implements the SIRF as a distributed PF composed of several processing elements and central unit. All the proposed architectures are captured using VHDL synthesized using Xilinx environment, and verified using the ModelSim simulator. Synthesis results confirmed the resource reduction and speed up advantages of our architectures.

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A Real-Time Object Tracking System Using a Particle Filter

Cited by 64 publications

References 14 publications

New trends on moving object detection in video images captured by a moving camera: A survey

New trends on moving object detection in video images captured by a moving camera: A survey

Video Face Recognition From A Single Still Image Using an Adaptive Appearance Model Tracker

Proposed hardware architectures of particle filter for object tracking

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