SIFT와 다중측면히스토그램을 이용한 다중물체추적

Multiple objects tracking is a challenging task. This article presents an algorithm which can detect and track multiple objects, and update target model automatically. The contributions of this paper as follow: Firstly,we also use color histogram(CH) and histogram of orientated gradients(HOG) to represent the objects, model update is realized by kalman filter and gaussian model; secondly we use Gaussian Mixture Model(GMM) and Bhattacharyya distance to detect object appearance. Particle filter with combined features and model update mechanism can improve tracking results. Experiments on video sequences demonstrate that the method presented in this paper can realize multiple objects detection and tracking.

Multiple object tracking is one field of study in vision-based navigation. Basically, in many current-research of this study, the common method in object tracking consists two main parts such as object detection and motion prediction, Object detection is highly related to the image processing which has a high tendency in using biggest part of computation resources. But, for the certain purpose, the computation resources should be used in proper way. In this paper, the problem will be limited in the case of football player tracking which needs so many computation resources for the image processing such the motion prediction algorithm that should be significantly reduced this need without decrease the system performance. In this paper, there will be explained the implementation of modified Hungarian algorithm and Kalman Filter as the main core of the motion prediction system in this case. Kalman Filter is the simplest algorithm for motion prediction, but in the case of multiple objects, there should be an additional computing to identify detected object before it will be tracked. Besides that, the system should have an ability to maintain object's identity in many worst case like occlusion, and boundary condition. In this paper, there also be explained the performance and consideration to increase system performance. In the highlight, the implementation result state that multiple point tracking module successfully being implemented as a part of fast multiple object tracking systems for football. The main indicator is identity maintainability which reaches 10% in 800 sample frames. Besides that, the computation parameter could be quantified using statistics that include the threshold value for data association is 2 meters per frame and 6 meters per frame for occlusion threshold. In the computational resources needs analysis, using worst case approximation, the system will need approximately 26 KB memory for computing in each frame. By this result, the conclusion is that the tracking system is proper to be implemented in football player tracking system both from the computation resources need and the performance.

Multiple object tracking is one field of study in vision-based navigation. Basically, in many current-research of this study, the common method in object tracking consists two main parts such as object detection and motion prediction, Object detection is highly related to the image processing which has a high tendency in using biggest part of computation resources. But, for the certain purpose, the computation resources should be used in proper way. In this paper, the problem will be limited in the case of football player tracking which needs so many computation resources for the image processing such the motion prediction algorithm that should be significantly reduced this need without decrease the system performance. In this paper, there will be explained the implementation of modified Hungarian algorithm and Kalman Filter as the main core of the motion prediction system in this case. Kalman Filter is the simplest algorithm for motion prediction, but in the case of multiple objects, there should be an additional computing to identify detected object before it will be tracked. Besides that, the system should have an ability to maintain object's identity in many worst case like occlusion, and boundary condition. In this paper, there also be explained the performance and consideration to increase system performance. In the highlight, the implementation result state that multiple point tracking module successfully being implemented as a part of fast multiple object tracking systems for football. The main indicator is identity maintainability which reaches 10% in 800 sample frames. Besides that, the computation parameter could be quantified using statistics that include the threshold value for data association is 2 meters per frame and 6 meters per frame for occlusion threshold. In the computational resources needs analysis, using worst case approximation, the system will need approximately 26 KB memory for computing in each frame. By this result, the conclusion is that the tracking system is proper to be implemented in football player tracking system both from the computation resources need and the performance.

Adaptive tracking-by-detection methods use previous tracking results to generate a new training set for object appearance, and update the current model to predict the object location in subsequent frames. Such approaches are typically bootstarpped by manual or semi-automatic initialization in the first several frames. However, most adaptive tracking-bydetection methods focus on tracking of a single object or multiple unrelated objects. Although one can trivially engage several single object trackers to track multiple objects, such solution is frequently suboptimal because it does not utilize the inter-object constraints or the obejct layout information [2]. We propose in this paper an adaptive tracking-by-detection method for multiple objects, inspired by recent work in [1] and [2]. The constraints for structured Support Vector Machine (SVM) in [1] are modified to localize multiple objects simultaneously with both appearance and layout information. Moreover, additional binary constraints are introduced to detect the existences of respective objects and to prevent possible model drift. Thus the method can handle frequent occlusion in multiple object tracking, as well as objects entering or leaving the scene. Those binary constraints make the optimization problem significantly different from the original Structured SVM [3]. The inter-object constraints, embedded in a linear programming technique similar to [2] for optimal position assignment, are applied to diminish false detections. In single object tracking case, given a set of frames {x1,x2, . . . ,xn} indexed by time, and the corresponding set of labeling, i.e. bounding box, {y1,y2, . . . ,yn}, structured SVM tries to find a model f (x,y), such that the task of predicting object location in a testing frame x could be conquered by maximizing: f (x,y) = 〈w,Ψ(x|y)〉, (1)

In order to achieve real-time extraction of feature point in the target detection and tracking system, we propose improved SIFT feature extraction algorithm based on FPGA hardware platform, which focuses on Pyramid structures and Gaussian convolution, at the same time, through the reasonable selection of algorithm parameter and fixed-point number , we can ensure the precision of the algorithm. In addition, we can achieve coordination between the various modules by multiplexing the SRAM. Experimental results indicate that the improved SIFT algorithm on FPGA hardware platform has high stability, low algorithm complexity and high accuracy, meanwhile, shows up good real-time capability.

In dynamic environments, a central task of the attentional system is to keep track of objects changing their spatial location over time. In some instances, it is sufficient to track only the spatial locations of moving objects (i.e., multiple object tracking; MOT). In other instances, however, it is also important to maintain distinct identities of moving objects (i.e., multiple identity tracking; MIT). Despite previous research, it is not clear whether MOT and MIT performance emerge from the same tracking mechanism. In the present report, we study gaze coherence (i.e., the extent to which participants repeat their gaze behaviour when tracking the same object locations twice) across repeated MOT and MIT trials. We observed more substantial gaze coherence in repeated MOT trials compared to the repeated MIT trials or mixed MOT-MIT trial pairs. A subsequent simulation study suggests that MOT is based more on a grouping mechanism than MIT, whereas MIT is based more on a target-jumping mechanism than MOT. It thus appears unlikely that MOT and MIT emerge from the same basic tracking mechanism.

This study aimed to assess the impact of manipulating postural stability during the execution of the multiple objects tracking (MOT) task on gaze behaviour. Twenty-eight males performed the MOT task under three levels of postural stability while eye movements were recorded. Postural stability had a significant impact on MOT scores and gaze behaviour. Participants exhibited worse MOT performance (p < 0.001, η² = 0.20) in less stable conditions, showing a reduced fixation duration (p = 0.020, η² = 0.09) and increased saccade frequency (p = 0.013, η² = 0.10). As stability conditions decreased, participants shifted their gaze strategies by an average of 12.34% of the time distribution from maintaining fixation on a central location to adopting the target-looking strategy. Additionally, the decline in MOT scores with decreasing stability was correlated with a reduction in fixation duration (r = 0.409, p = 0.031). The findings evidence that gaze behaviour during MOT is dependent on postural stability and MOT performance is influenced by the chosen gaze strategy. Key points Reduced postural stability significantly impairs the ability to track multiple objects. Changes in postural stability during a multiple object tracking task affect gaze behaviour by alterations in fixation duration and saccadic frequency. Decrease in multiple object tracking task scores is associated with alterations in gaze behaviour, specifically a reduction in fixation duration. Participants shifted their gaze strategies from maintaining fixation on a central location to adopting a saccadic target-looking strategy as stability conditions decreased.

We propose a real-time multiple object tracking method for smart environment applications. The proposed method combines identity features and tracking features for robust long-term object tracking. Face is a stable feature for identifying different subjects. However, face is not reliable for object tracking. Whole body color histogram is more resistant to object scale and pose changes, which has been widely applied in short-term object tracking. Unfortunately, color histograms are not stable over a relatively long period of time. In smart environments, same subjects may have different clothes over a long time span. We propose a method to combine these two features, the identity features and tracking features, to achieve reliable multiple object tracking in smart environments. A fast object labeling approach is proposed to track multiple objects in real time. The proposed method has clear advantage over traditional single feature methods. Experiments confirm that the proposed method can reliably track multiple human objects in real time through long video sequences.

Real-time tracking of multiple objects in space-variant vision based on magnocellular visual pathway

This paper introduces a joint learning architecture (JLA) for multiple object tracking (MOT) and multiple object forecasting (MOF) in which the goal is to predict tracked objects’ current and future locations simultaneously. MOF is a recent formulation of trajectory forecasting where the full object bounding boxes are predicted rather than trajectories alone. Existing works separate multiple object tracking and multiple object forecasting. Such an approach can propagate errors in tracking to forecasting. We propose a joint learning architecture for multiple object tracking and forecasting (MOTF). Our approach reduces the chances of propagating tracking errors to the forecasting module. In addition, we show, through a new data association step, that forecasting predictions can be used for tracking objects during occlusion. We adapt an existing MOT method to simultaneously predict current and future object locations and confirm that JLA benefits both the MOT and MOF tasks.

Multiple Object Tracking(MOT) developed by Pylyshyn and Storm(1988) has been widely used in the study of capacity-limited and object-based attention. Many researchers interested in finding out whether or not the tracking processes in MOT occupy attentional resource and what type of resource is used. The typical paradigm used in this line of research is dual-task paradigm. Participants were asked to perform a visual or auditory task and the MOT task simultaneously. MOT performance was interfered by both the visual and the auditory task. However, the interference of visual task and auditory task with MOT occurred at different levels. The previous studies demonstrated that the MOT task and visual task occupy visual attention resources. Although the MOT task and auditory task don't occupy visual attention resources, they share more central attention resources(such as executive function). Multiple Identity Tracking(MIT) is a variant of MOT, in which each object carries a unique identity. The previous studies also included both visual or auditory task and MIT task, but those studies have not examined how the visual/auditory task affects the MIT task when the two tasks shared the same properties. The current study included 3 experiments and aimed to investigate the influence of a visual or auditory task on either MOT or MIT task. The first two experiments manipulated participants' eye movement to compare the different effects of visual task and auditory task on MOT performance. The result of experiment 1A showed that the auditory task interfered more with MOT than did the visual task when eyes were fixated at the center of the screen. However, the auditory task yielded less interference with MOT when there was no eye movement control in experiment 1B. The results indicated that the tracking processes in MOT not only occupy visual attention resources, but also occupy central attention resources(such as executive function). The experiment 2 applied visual and auditory digit judgment task and MIT task. When the object identity(marked by a digit) was identical to the number in the digit judgment, the MIT performance was facilitated by both the visual and auditory task. It is possible that the process of digit activated the target identities(the same digits) that was stored in visual working memory during tracking.

Presents a novel approach to the tracking of multiple articulate objects in the presence of occlusion in moderately complex scenes. Most conventional tracking algorithms work well when only one object is tracked at a time. However, when multiple objects must be tracked simultaneously, significant computation is often introduced in order to handle occlusion and to calculate the appropriate region correspondence between successive frames. We introduce a near-real-time solution to this problem by using a probabilistic mixing of low-level features and components. The algorithm mixes coarse motion estimates, change detection information and unobservable predictions to create accurate trajectories of moving objects. We implement this multifeature mixing strategy within the context of a video surveillance system using a modified Kalman filtering mechanism. Experimental results demonstrate the efficacy of the proposed tracking and surveillance system.

Model-free object tracking is still challenging because of the limited prior knowledge and the unexpected variation of the target object. In this paper, we propose a feature learning algorithm for model-free multiple object tracking. First, we pre-learn generic features invariant to diverse motion transformations from auxiliary video data by using a deep network of anto-encoder. Then, we adapt the pre-learned features according to multiple target objects respectively in a multi-task learning manner. We treat the feature adaptation for each target object as one single task. We simultaneously learn the common feature shared by all target objects and the individual feature of each object. Experimental results demonstrate that our feature learning algorithm can significantly improve multiple object tracking performance.

We describe a novel probabilistic framework for real-time tracking of multiple objects from combined depth-colour imagery. Object shape is represented implicitly using 3D signed distance functions. Probabilistic generative models based on these functions are developed to account for the observed RGB-D imagery, and tracking is posed as a maximum a posteriori problem. We present first a method suited to tracking a single rigid 3D object, and then generalise this to multiple objects by combining distance functions into a shape union in the frame of the camera. This second model accounts for similarity and proximity between objects, and leads to robust real-time tracking without recourse to bolt-on or ad-hoc collision detection.

In this paper, we present an algorithm for the tracking of multiple objects in space-variant vision. Typically, an object-tracking algorithm consists of several processes such as detection, prediction, matching, and updating. In particular, the matching process plays an important role in multiple objects tracking. In traditional vision, the matching process is simple when the target objects are rigid. In space-variant vision, however, it is very complicated although the target is rigid, because there may be deformation of an object region in the space-variant coordinate system when the target moves to another position. Therefore, we propose a deformation formula in order to solve the matching problem in space-variant vision. By solving this problem, we can efficiently implement multiple objects tracking in space-variant vision.