Multistage Particle Windows for Fast and Accurate Object Detection

Gualdi, Giovanni; Prati, Andrea; Cucchiara, Rita

doi:10.1109/tpami.2011.247

Cited by 48 publications

(25 citation statements)

References 49 publications

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“…24 The sliding window search is the common paradigm employed for object detection and can locate the coupler yoke in our proposed method with high accuracy (see Fig. 8).…”

Section: Localization Of Coupler Yokesmentioning

confidence: 99%

Automatic online vision-based inspection system of coupler yoke for freight trains

Zheng

Wei

2016

J. Electron. Imaging

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, "Automatic online vision-based inspection system of coupler yoke for freight trains," J. Electron. Imaging 25(6), 061602 (2016), doi: 10.1117/1.JEI.25.6.061602. Abstract. Fault inspection plays an important role in ensuring the safe operation of freight trains. With the development of computer vision technology, the vision-based fault inspection has become one of the principal means of fault inspection. A coupler yoke is an important component of the train's connection system, and faults in this system would cause the separation of the train, leading to a serious accident. We propose an automatic image inspection system to inspect for faults in coupler yokes during the running of a freight train. The inspection process is divided into two parts: the localization part and the recognition part. In the localization part, we propose multiple dimension features, design a fast algorithm to compute multiresolution image features, and use a linear support vector machine classifier to locate the position of the coupler yoke. In the recognition part, we propose a fast decision tree training method by prepruning noneffective features, and use Adaboost decision trees as the final fault classifier. Experimental results show that this proposed method can achieve a fault inspection rate of 98.6% while the average processing time of an image is about 98 ms, which shows our system has a high inspection accuracy and a good real-time performance.

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“…24 The sliding window search is the common paradigm employed for object detection and can locate the coupler yoke in our proposed method with high accuracy (see Fig. 8).…”

Section: Localization Of Coupler Yokesmentioning

confidence: 99%

Automatic online vision-based inspection system of coupler yoke for freight trains

Zheng

Wei

2016

J. Electron. Imaging

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“…These applications include, but are not limited to, visual tracking [25] [26], object detection [27], image segmentation [28], contour detection [29], video stabilization [30], and even point set registration [31], i.e. finding a spatial transformation that aligns two given point sets.…”

Section: Hil Functional Validationmentioning

confidence: 99%

“…the time the system needs to generate a valid estimation from a valid measurement. The estimation error is evaluated as the Euclidean distance (or 2-norm distance) between both estimation and measurement points, which is shown in (27) c xk and z yk are the measurements, whereas Xk and i¡k are the estimations. System parameters in this work can be divided into two different groups: model parameters (e.g.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Evolutionary Computing and Particle Filtering: A Hardware-Based Motion Estimation System

Rodríguez

Moreno

2015

IEEE Trans. Comput.

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Abstract-Particle filters constitute themselves a highly powerful estimation tool, especially when dealing with non-linear non-Gaussian systems. However, traditional approaches present several limitations, which reduce significantly their performance. Evolutionary algorithms, and more specifically their optimization capabilities, may be used in order to overcome particle-filtering weaknesses. In this paper, a novel FPGA-based particle filter that takes advantage of evolutionary computation in order to estimate motion patterns is presented. The evolutionary algorithm, which has been included inside the resampling stage, mitigates the known sample impoverishment phenomenon, very common in particle-filtering systems. In addition, a hybrid mutation technique using two different mutation operators, each of them with a specific purpose, is proposed in order to enhance estimation results and make a more robust system. Moreover, implementing the proposed Evolutionary Particle Filter as a hardware accelerator has led to faster processing times than different software implementations of the same algorithm.

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“…Many researchers have made contributions to the progress of object detection [1][2][3][5][6][7][8]10,15,16,18,19,[22][23][24][25][26][27]31]. When it comes to how to generate image patches, we generally cast the object detection methods into three categories.…”

Section: Related Workmentioning

confidence: 99%

“…Another image patch generation approach is multistage particle-window (MS-PW ) proposed by Gualdi et al [10]. This paradigm castes object detection into a statisticalbased search using Monte Carlo sampling for estimating the distribution of target objects.…”

Section: Related Workmentioning

confidence: 99%