Optical Flow Tracking Research Articles

Full-field OCT technique for high speed event-based optical flow and particle tracking.

This article introduces a method to extract the speed and density of microparticles in real time at several kHz using an asynchronous event-based camera mounted on a full-field optical coherence tomography (FF-OCT) setup. These cameras detect significant amplitude changes, allowing scene-driven acquisitions. They are composed of an array of autonomously operating pixels. Events are triggered when an illuminance change at the pixel level is significant at 1μs time precision. The event-driven FF-OCT algorithm relies on a time-based optical flow computation to operate directly on incoming events and updates the estimation of velocity, direction and density while reducing both computation and data load. We show that for fast moving microparticles in a range of 0.4 - 6.5mm/s, the method performs faster and more efficiently than existing techniques in real time. The target application of this work is to evaluate erythrocyte dynamics at the microvascular level in vivo with a high temporal resolution.

A novel fusion framework for robust human tracking by a service robot

This work investigates the problem of robust vision-based human tracking by a human following robot using point-based features like SURF. The problem is challenging owing to failures arising because of variation in illumination, change in pose, size or scale, camera motion and partial or full occlusion. While point-based features provide robust detection against photometric and geometric distortions, the tracking of these features over subsequent frames becomes difficult as the number of matching points between a pair of images drops quickly with slight variation in target attribute owing to above mentioned variations. The problem of robust human tracking by the robot is solved by proposing a multi-tracker fusion framework that allows one to combine multiple tracker to ensure long term tracking of the target. This fusion framework also allows for creating a dynamic template pool of target features which gets updated over time. The interaction between the first two trackers is used to update the template pool of the target attribute while the last tracker is used to estimate the location of the target in case of full occlusion. The working of the framework is demonstrated by combining a SURF-based mean-shift tracker, an optical-flow tracker and a Kalman filter to provide robust tracking over a long time. The efficacy of the resulting tracker is demonstrated through rigorous testing on a variety of video datasets.

High-resolution traction force microscopy on small focal adhesions - improved accuracy through optimal marker distribution and optical flow tracking

The accurate determination of cellular forces using Traction Force Microscopy at increasingly small focal attachments to the extracellular environment presents an important yet substantial technical challenge. In these measurements, uncertainty regarding accuracy is prominent since experimental calibration frameworks at this size scale are fraught with errors – denying a gold standard against which accuracy of TFM methods can be judged. Therefore, we have developed a simulation platform for generating synthetic traction images that can be used as a benchmark to quantify the influence of critical experimental parameters and the associated errors. Using this approach, we show that TFM accuracy can be improved >35% compared to the standard approach by placing fluorescent beads as densely and closely as possible to the site of applied traction. Moreover, we use the platform to test tracking algorithms based on optical flow that measure deformation directly at the beads and show that these can dramatically outperform classical particle image velocimetry algorithms in terms of noise sensitivity and error. We then report how optimized experimental and numerical strategy can improve traction map accuracy, and further provide the best available benchmark to date for defining practical limits to TFM accuracy as a function of focal adhesion size.

Nonrigid Optical Flow Ground Truth for Real-World Scenes With Time-Varying Shading Effects

In this paper we present a dense ground truth dataset of nonrigidly deforming real-world scenes. Our dataset contains both long and short video sequences, and enables the quantitatively evaluation for RGB based tracking and registration methods. To construct ground truth for the RGB sequences, we simultaneously capture Near-Infrared (NIR) image sequences where dense markers - visible only in NIR - represent ground truth positions. This allows for comparison with automatically tracked RGB positions and the formation of error metrics. Most previous datasets containing nonrigidly deforming sequences are based on synthetic data. Our capture protocol enables us to acquire real-world deforming objects with realistic photometric effects - such as blur and illumination change - as well as occlusion and complex deformations. A public evaluation website is constructed to allow for ranking of RGB image based optical flow and other dense tracking algorithms, with various statistical measures. Furthermore, we present an RGB-NIR multispectral optical flow model allowing for energy optimization by adoptively combining featured information from both the RGB and the complementary NIR channels. In our experiments we evaluate eight existing RGB based optical flow methods on our new dataset. We also evaluate our hybrid optical flow algorithm by comparing to two existing multispectral approaches, as well as varying our input channels across RGB, NIR and RGB-NIR.

Vision-Based Vehicle and Pedestrian Tracking of Intersection Videos

Vehicle and pedestrian tracking is a key component for vision-based safety analysis which can use motion and appearance cues of road users. Since appearance-based detectors generate false alarms and provide lower speed, motion-based detectors are preferred but they work poorly when pedestrians or vehicles stop due to traffic signals. In this paper, a tracking system is proposed to track waiting and moving road users by fusion of motion and appearance cues at detection level. The enhanced optical flow tracker handles the partial occlusion problem, and it cooperates with the detection module to provide long-term tracks of vehicles and pedestrians. The system evaluation shows 13% and 43% improvement in tracking of vehicles and pedestrians respectively, and finally heat-maps show benefits of using the proposed system through the visual depiction of intersection usage.

Homography-based block motion estimation for video coding of PTZ cameras

Due to the constrained movement of pan-tilt-zoom (PTZ) cameras, two frames in the video sequences captured by such cameras can be geometrically related by a relationship (homography). This geometric relationship is helpful for reducing the spatial redundancy in video coding. In this paper, by exploiting the homography between two frames with optical flow tracking algorithm, we propose a novel homography-based search (HBS) algorithm for block motion estimation in coding the sequences captured by PTZ cameras. In addition, adaptive thresholds are adopted in our method to classify different kinds of blocks. Compared with other traditional fast algorithms, the proposed HBS algorithm is proved to be more efficient for the sequences captured by PTZ cameras. And compared to our previous work in ICME (Cui et al., 2011), which only deals with pan-tilt (PT) camera and calculates the homography with mechanical devices, in this extended work we compute the homography by using information on images instead.

Object-guided motion estimation

Motion estimation in image sequences is classically addressed under one of the two following forms: estimation of optical flow (instantaneous apparent motion over the whole image) and visual tracking (estimation of the motion of a certain scene region over time). Major progresses have been recently achieved on both fronts, with robust and accurate techniques available for each problem. However, these problems are mostly studied as if they were independent, while they address in fact two faces of the same reality. This paper analyzes the benefits and consequences of combining tracking methods for estimation of per-object dense motion trajectories. We show experimentally that studying the global translation of an object will benefit the motion estimation accuracy of sample points inside the larger structure.

A multi-target trapping and tracking algorithm for Bactrocera Dorsalis based on cost model

Researching a robust, high precision, stable performance and applicable target tracking method is of important theoretical significance and practical value. Using computer vision technology to detect and track moving object in video image is a kind of advanced target counting method and it is a research hotspot. That is a complex estimation problem and still faces enormous challenges. Bactrocera dorsalis, which is widely distributed in the southern area of China, has become the most serious pest and need to be controlled frequently. In this study, a B. dorsalis tracking and counting system based on computer vision has been built to find out the activity patterns of B. dorsalis so as to provide a better prevention and control of the pest. For counting the number of B. dorsalis passed through the monitoring area in real time, this study has analyzed the advantages and disadvantages of the mainstream methods, such as inter-frame difference method, background-difference method, optical flow tracking method, corner detection method, Mean Shift tracking method and other classical target tracking algorithms. Based on these analyses, this study has proposed a cost model for tracking B. dorsalis, which can track all the trajectories of B. dorsalis in the monitoring area in real time. A multi-B. dorsalis tracking algorithm based on this cost model and a B. dorsalis counting algorithm have been realized. Finally, the result of the experiment shows that the algorithm can solve the overlap of B. dorsalis. Additionally, a field experiment of the tracking algorithm has been carried out for 5days in Guangzhou Yangtao Park. The number of B. dorsalis detected by computer system was 1570 and the manually counted number was 1591.The tracking accuracy is 98.7%.

An improved optical flow tracking technique for real-time MR-guided beam therapies in moving organs

Magnetic resonance (MR) guided high intensity focused ultrasound and external beam radiotherapy interventions, which we shall refer to as beam therapies/interventions, are promising techniques for the non-invasive ablation of tumours in abdominal organs. However, therapeutic energy delivery in these areas becomes challenging due to the continuous displacement of the organs with respiration. Previous studies have addressed this problem by coupling high-framerate MR-imaging with a tracking technique based on the algorithm proposed by Horn and Schunck (H and S), which was chosen due to its fast convergence rate and highly parallelisable numerical scheme. Such characteristics were shown to be indispensable for the real-time guidance of beam therapies. In its original form, however, the algorithm is sensitive to local grey-level intensity variations not attributed to motion such as those that occur, for example, in the proximity of pulsating arteries.In this study, an improved motion estimation strategy which reduces the impact of such effects is proposed. Displacements are estimated through the minimisation of a variation of the H and S functional for which the quadratic data fidelity term was replaced with a term based on the linear L1norm, resulting in what we have called an L2–L1 functional.The proposed method was tested in the livers and kidneys of two healthy volunteers under free-breathing conditions, on a data set comprising 3000 images equally divided between the volunteers. The results show that, compared to the existing approaches, our method demonstrates a greater robustness to local grey-level intensity variations introduced by arterial pulsations. Additionally, the computational time required by our implementation make it compatible with the work-flow of real-time MR-guided beam interventions.To the best of our knowledge this study was the first to analyse the behaviour of an L1-based optical flow functional in an applicative context: real-time MR-guidance of beam therapies in moving organs.

Automated measurement of fast mitochondrial transport in neurons.

There is growing recognition that fast mitochondrial transport in neurons is disrupted in multiple neurological diseases and psychiatric disorders. However, a major constraint in identifying novel therapeutics based on mitochondrial transport is that the large-scale analysis of fast transport is time consuming. Here we describe methodologies for the automated analysis of fast mitochondrial transport from data acquired using a robotic microscope. We focused on addressing questions of measurement precision, speed, reliably, workflow ease, statistical processing, and presentation. We used optical flow and particle tracking algorithms, implemented in ImageJ, to measure mitochondrial movement in primary cultured cortical and hippocampal neurons. With it, we are able to generate complete descriptions of movement profiles in an automated fashion of hundreds of thousands of mitochondria with a processing time of approximately one hour. We describe the calibration of the parameters of the tracking algorithms and demonstrate that they are capable of measuring the fast transport of a single mitochondrion. We then show that the methods are capable of reliably measuring the inhibition of fast mitochondria transport induced by the disruption of microtubules with the drug nocodazole in both hippocampal and cortical neurons. This work lays the foundation for future large-scale screens designed to identify compounds that modulate mitochondrial motility.

이동물체들의 Optical flow와 EMD 알고리즘을 이용한 식별과 Kalman 필터를 이용한 추적

이동물체들의 Optical flow와 EMD 알고리즘을 이용한 식별과 Kalman 필터를 이용한 추적

Facial expression recognition from image sequences using twofold random forest classifier

We propose a novel framework for facial expressions analysis by recognizing AUs from image sequences using twofold random forest classifier in this paper. The measurement of facial motion is through tracking of Active Appearance Model (AAM) facial feature points using Lucas–Kanade (LK) optical flow tracker by estimating the displacements of the feature points. The displacement vectors between the neutral expression frame and the peak expression frame are used as motion features of facial expression. They will then be transformed to the first level random forest to determine the Action Units (AUs) of the corresponding expression sequences. Finally, the detected AUs are inputed into the second level random forest for facial expressions classification. The experiments on Extended Cohn–Kanade(CK+) database demonstrate that the proposed method can achieve higher performance than several other approaches on both AUs and facial expression recognition. We attain an average recognition rate of AUs and facial expression of 100% and 96.38% respectively.

A REVIEW ON MOVING OBJECT DETECTION AND TRACKING METHODS

Real time motion detection has very wide application in human motion recognition or vehicle motion recognition. Patient monitoring, human–computer interaction are the recent applications of the human motion recognition, and is recent application of vehicle motion recognition are vehicle counting and very helpful in traffic control by detecting vehicle as per size, color, speed. In video surveillance real time motion recognition is very difficult so I want to work on real time motion recognition. Based on above discussion in my research work I have decided to develop intelligent framework for Real time moti on detection or recognition for appropriate thing or object. Still real time image based or video based motion detection are hidden area so I want to carry out my work on real time video based detect behavior of human as per movement of object or vehicle detection in sense of hybrid. It will prove very helpful for public safety. This paper combines the GMM and Optical Flow object detection and tracking. In this paper we combine GMM and Optical Flow method. GMM can be used in the context of a complex environment while Optical Flow can be used for quick calculation with simple background. GMM is not a complete object tracking while Object Flow provides complete computation tracking.

Application of Object Detection and Tracking Techniques for Unmanned Aerial Vehicles

In this research, the information captured by Unmanned Aerial Vehicles (UAVs) are eminently utilized in detecting and tracking moving objects which pose a primary security threat against the United States southern border. Illegal trespassing and border encroachment by immigrants is a huge predicament against the United States border security force and the Department of Homeland Security. It becomes insurmountable to warranty suspicious behaviour, monitoring by human operators for long periods of time, due to the massive amount of data involved. The main objective of this research is to assist the human operators, by implementing intelligent visual surveillance systems which help in detecting and tracking suspicious or unusual events in the video sequence. The visual surveillance system requires fast and robust methods of detecting and tracking moving objects. In this research, we have investigated methods for detecting and tracking objects from UAVs. Moving objects were detected using adaptive background subtraction technique successfully and these detected objects were tracked by using Lucas-Kanade optical flow tracking, Continuously Adaptive Mean-Shift tracking based techniques. The simulation results show the efficacy of these techniques in detecting and tracking moving objects in the video sequences acquired by the UAV.

Evaluation of Optical Flow Tracking Techniques in Cardiac Magnetic Resonance: Numerical Study

Evaluation of Optical Flow Tracking Techniques in Cardiac Magnetic Resonance: Numerical Study

Robust Autonomous Car-Like Robot Tracking Based on Tracking-Learning-Detection

This paper proposes a robust tracking algorithm for an autonomous car-like robot, and this algorithm is based on the Tracking-Learning-Detection (TLD). In this paper, the TLD method is extended to track the autonomous car-like robot for the first time. In order to improve accuracy and robustness of the proposed algorithm, a method of symmetry detection of autonomous car-like robot rear is integrated into the TLD. Moreover, the Median-Flow tracker in TLD is improved with a pyramid-based optical flow tracking method to capture fast moving objects. Extensive experiments and comparisons show the robustness of the proposed method.

Hybrid intensity- and phase-based optical flow tracking of tagged MRI.

Accurate tracking of the myocardium tissues in tagged Magnetic Resonance Images (MRI) is essential for evaluating the cardiac function. Current tracking methods utilize either the image intensity or the image phase as landmarks that can be tracked. In either case, the performance is vulnerable to the image quality and the fading of the tag lines. In this work, we propose a hybrid optical flow tracking method that combines both the intensity and the phase features of the image. The method is validated using numerical cardiac phantom as well as real MRI data experiments. Both experiments showed that the proposed method outperforms current intensity-based optical flow tracking and the phase-based HARP method with maximum error of 1 pixel at extreme conditions of tag fading.

Confidence Based Hierarchical Online Ensemble Visual Tracking

In order to adapt to the target appearance changes during visual tracking, feature model needs to be updated by online learning. However, online adaptive methods suffer from the drifting problem: error data are used for updating and will finally lead to tracking failure. In this paper, we propose a novel hierarchical online ensemble tracking method. Optical flow tracker is employed to predict the rough location. Online learning classifier is employed to detect the target. Template match is used to filter the data for updating. All these parts are combined together hierarchically by their confidence to ensure the stability of online learning and tracking. In contrast to the individual online learning and semi-supervised online learning, our method show more adaptability and stability. We demonstrate the performance on several public video sequences, which shows the improvement of our method over other online tracking approaches.

Greedy framework for optical flow tracking of myocardium contours

Optical flow (OF) tracking of the myocardium contours has a potential in segmenting the myocardium in time sequences of cardiac medical images. Nevertheless, to estimate the displacement field of the contour points, a number of assumptions are required to solve an under-determined set of optical flow equations. In this work, a new framework is proposed to solve the OF tracking problem using greedy optimisation algorithm. The new framework allows different types of constraints such as motion invariance, shape and topology to be applied in a unified way. The developed methods are applied to a publicly-available cardiac magnetic resonance imaging dataset containing image sequences for 33 patients. Quantitative evaluation of the results shows high potential of the methods to accurately track and segment the myocardium contours.

Random sampling algorithm in the image registration problem

Image registration is one of the basic problems of computer vision. It arises in optical flow estimation, stereo vision, and tracking problems. One of the classical approaches proposed by B. Lucas and T. Kanade is based on optimization of some cost function. In this article image registration algorithm based on Lucas–Kanade approach is proposed (random sampling algorithm). It shows high performance results.