Fast Vehicle Detection using Correlation Filters

Sangpil Han,Min-jae Kim,Seokmok Park,Joonki Paik 대한전자공학회 2017 IEIE Transactions on Smart Processing & Computing Vol.6 No.5

Object detection is very challenging research in the computer vision community, and vehicle detection has become an important issue in various applications, such as unmanned systems and intelligent transportation systems. Most of these applications require fast and accurate vehicle detection. In recent years, it has been proven that correlation filters can find target objects fast and accurately owing to Parseval’s theorem and dense sampling. However, we think that the existing correlation filters have not used all the helpful information. Therefore, we propose a robust and fast vehicle detection method based on an improved correlation filter framework that exploits the additional information from correlation filters. The proposed vehicle detection algorithm consists of five steps: i) training the correlation filter, ii) correlation of input and the trained filter, iii) finding local maxima as vehicle candidates in the correlation output, iv) filtering the candidates by using the shape and sharpness of the maxima, and v) estimation of the location and scale of the vehicles. The proposed algorithm runs fast and accurately, so it can be applied to many other applications, such as object alignment, object detection, and object tracking. We evaluated the proposed algorithm performance by comparing it with the state-of-the-art correlation filter-based methods.

Size Aware Correlation Filter Tracking with Adaptive Aspect Ratio Estimation

( Xiaozhou Zhu ),( Xin Song ),( Xiaoqian Chen ),( Yuzhu Bai ),( Huimin Lu ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.2

Correlation Filter-based Trackers (CFTs) gained popularity recently for their effectiveness and efficiency. To deal with the size changes of the target which may degenerate the tracking performance, scale estimation has been introduced in existing CFTs. However, the variations of the aspect ratio were usually neglected, which also influence the size of the target. In this paper, Size Aware Correlation Filter Trackers (SACFTs) are proposed to deal with this problem. The SACFTs not only determine the translation and scale variations, but also take the aspect ratio changes into consideration, thus a better estimation of the size of the target can be realized, which improves the overall tracking performance. And competing results can be achieved compared with state-of-the-art methods according to the experiments conducted on two large scale datasets.

마스크 생산 라인에서 영상 기반 마스크 필터 검사를 위한 계층적 상관관계 기반 이상 현상 탐지

오건희,이효진,이헌철 대한임베디드공학회 2021 대한임베디드공학회논문지 Vol.16 No.6

This paper addresses the problem of vision-based mask filter inspection for mask production systems. Machine learning-based approaches can be considered to solve the problem, but they may not be applicable to mask filter inspection if normal and anomaly mask filter data are not sufficient. In such cases, handcrafted image processing methods have to be considered to solve the problem. In this paper, we propose a hierarchical correlation-based approach that combines handcrafted image processing methods to detect anomaly mask filters. The proposed approach combines image rotation, cropping and resizing, edge detection of mask filter parts, average blurring, and correlation-based decision. The proposed approach was tested and analyzed with real mask filters. The results showed that the proposed approach was able to successfully detect anomalies in mask filters.

Speech Feature Selection of Normal and Autistic children using Filter and Wrapper Approach

Akhtar, Muhammed Ali,Ali, Syed Abbas,Siddiqui, Maria Andleeb International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.5

Two feature selection approaches are analyzed in this study. First Approach used in this paper is Filter Approach which comprises of correlation technique. It provides two reduced feature sets using positive and negative correlation. Secondly Approach used in this paper is the wrapper approach which comprises of Sequential Forward Selection technique. The reduced feature set obtained by positive correlation results comprises of Rate of Acceleration, Intensity and Formant. The reduced feature set obtained by positive correlation results comprises of Rasta PLP, Log energy, Log power and Zero Crossing Rate. Pitch, Rate of Acceleration, Log Power, MFCC, LPCC is the reduced feature set yield as a result of Sequential Forwarding Selection.

Deep Learning-based Multiple Pedestrians Detection-Tracking Framework

Xuan-Phung Huynh,Yong-Guk Kim 한국HCI학회 2016 한국HCI학회 학술대회 Vol.2016 No.1

We propose a new Detection-Tracking (DT) framework whereby one can detect a pedestrian, or multiple ones, within a video image and then track them concurrently in a flexible manner. For the detection, a faster R-CNN will be used since it has a state-of-the-art detection accuracy as well as speed. For the tracking, we have developed a fast and reliable tracker, which mainly consists of Kernelized Correlation Filter (KCF) and Kalman filter and shows enhancing performance in the occlusion and human-crossing situations. After the faster R-CNN detects objects’ regions and scores for that objects, our tracker estimates object’s position based on kernel method and Kalman filter. We demonstrate that the proposed framework can detect and track multiple moving pedestrians concurrently for the walking crowd scene.

Schmidt-Kalman Filters for Systems with Uncertain Parameters and Asynchronous Sampling

Jaroslav Taba?ek,Vladimir Havlena 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10

This paper introduces estimation algorithms for systems with uncertain parameters and asynchronous sampling. The algorithms are created by merging the Schmidt-Kalman filter (SKF) for systems with uncertain parameters and the conventional Kalman filter for systems with correlated noises. The system descriptions obtained by different discretization approaches are analyzed and used to develop the equivalent of the SKF. Then the SKF for systems with asynchronous sampling is developed by applying the SKF or its equivalent on the part of sampling period where the process and measurement noises are correlated. The accuracy of the novel filters is tested on a simple example.

Partial Matched Filter for Low Power and Fast Code Acquisition of DSSS-CPFSK Signals

Park, Hyung-Chul The Institute of Electronics and Information Engin 2004 Journal of semiconductor technology and science Vol.4 No.3

A partial matched filter (PMF) for semi-coherent correlation code acquisition of the DSSS-CPFSK signal is proposed. It is a calculation-reduced structure of the hard-limited signal based FIR filter, yet its code acquisition time is equal to that of the hard-limited signal based FIR filter. The PMF eliminates duplicate calculations by utilizing the characteristic that the hard-limited DSSS-CPFSK signal has same value in several consecutive samples. For example, the PMF can achieve about 95% reduction in gate size, as compared to the hard-limited signal based FIR filter, when the modulation index of the DSSS-CPFSK signal is equal to 1.5 and the sample rate is equal to 40 sample/chip.

IGBT Open-Circuit Fault Diagnosis for 3-Phase 4-Wire 3-Level Active Power Filters based on Voltage Error Correlation

Wang, Ke,Tang, Yi,Zhang, Xiao,Wang, Yang,Zhang, Chuan-Jin,Zhang, Hui The Korean Institute of Power Electronics 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.5

A novel open-circuit fault diagnosis method for 3-phase 4-wire 3-level active power filters based on voltage error correlation is proposed in this paper. This method is based on observing the output pole voltage error of the active power filter through two kinds of algorithms. One algorithm is a voltage error analytical algorithm, which derives four output voltage error analytic expressions through the pulse state, current value and dc bus voltage, respectively, assuming that all of the IGBTs of a certain phase come to an OC fault. The other algorithm is a current circuit equation algorithm, which calculates the real-time output voltage error through basic circuit theory. A correlation is introduced to measure the similarity of the output voltage errors between the two algorithms, and OC faults are located by the maximum of the correlations. A FPGA has been chosen to implement the proposed method due to its fast prototyping. Simulation and experimental results are presented to show the performance of the proposed OC fault diagnosis method.

Structurally Enhanced Correlation Tracking

( Mayur Rajaram Parate ),( Kishor M. Bhurchandi ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.10

In visual object tracking, Correlation Filter-based Tracking (CFT) systems have arouse recently to be the most accurate and efficient methods. The CFT's circularly shifts the larger search window to find most likely position of the target. The need of larger search window to cover both background and object make an algorithm sensitive to the background and the target occlusions. Further, the use of fixed-sized windows for training makes them incapable to handle scale variations during tracking. To address these problems, we propose two layer target representation in which both global and local appearances of the target is considered. Multiple local patches in the local layer provide robustness to the background changes and the target occlusion. The target representation is enhanced by employing additional reversed RGB channels to prevent the loss of black objects in background during tracking. The final target position is obtained by the adaptive weighted average of confidence maps from global and local layers. Furthermore, the target scale variation in tracking is handled by the statistical model, which is governed by adaptive constraints to ensure reliability and accuracy in scale estimation. The proposed structural enhancement is tested on VTBv1.0 benchmark for its accuracy and robustness.

Rao-Blackwellized Particle Filter for Asynchronously Dependent Noises

Yunqi Chen,Zhi-Bin Yan,Xing Zhang 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.6

This paper develops Rao-Blackwellized particle filter with asynchronous dependence between system noise and measurement noise. It is pointed out that this dependence affects both the particle filter update step for the nonlinear sub-system and the Kalman filter update step for the conditionally linear sub-system in Rao-Blackwellized particle filter. A de-correlation method is suggested to deal with such influence. The optimal importance density function for sampling the nonlinear sub-state is found out, and a suboptimal one for approximating the optimal importance density function is proposed. The proposed methods are applied to target tracking to testify their effectiveness and superiority.