Cascading Randomized Weighted Majority: A New Online Ensemble Learning Algorithm

With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm. Online ensemble methods are online algorithms which take advantage of an ensemble of classifiers to predict labels of data. Prediction with expert advice is a well-studied problem in the online ensemble learning literature. The Weighted Majority algorithm and the randomized weighted majority (RWM) are the most well-known solutions to this problem, aiming to converge to the best expert. Since among some expert, the best one does not necessarily have the minimum error in all regions of data space, defining specific regions and converging to the best expert in each of these regions will lead to a better result. In this paper, we aim to resolve this defect of RWM algorithms by proposing a novel online ensemble algorithm to the problem of prediction with expert advice. We propose a cascading version of RWM to achieve not only better experimental results but also a better error bound for sufficiently large datasets.Comment: 15 pages, 3 figure

Learning Spatial Distribution of Long-Term Trackers Scores

Long-Term tracking is a hot topic in Computer Vision. In this context, competitive models are presented every year, showing a constant growth rate in performances, mainly measured in standardized protocols as Visual Object Tracking (VOT) and Object Tracking Benchmark (OTB). Fusion-trackers strategy has been applied over last few years for overcoming the known re-detection problem, turning out to be an important breakthrough. Following this approach, this work aims to generalize the fusion concept to an arbitrary number of trackers used as baseline trackers in the pipeline, leveraging a learning phase to better understand how outcomes correlate with each other, even when no target is present. A model and data independence conjecture will be evidenced in the manuscript, yielding a recall of 0.738 on LTB-50 dataset when learning from VOT-LT2022, and 0.619 by reversing the two datasets. In both cases, results are strongly competitive with state-of-the-art and recall turns out to be the first on the podium.Comment: 20 pages, 11 figures, 3 table

Visual tracking with spatio-temporal Dempster-Shafer information fusion

A key problem in visual tracking is how to effectively combine spatio-temporal visual information from throughout a video to accurately estimate the state of an object. We address this problem by incorporating Dempster-Shafer information fusion into the tracking approach. To implement this fusion task, the entire image sequence is partitioned into spatially and temporally adjacent subsequences. A support vector machine (SVM) classifier is trained for object=non-object classification on each of these subsequences, the outputs of which act as separate data sources. To combine the discriminative information from these classifiers, we further present a spatio-temporal weighted Dempster-Shafer (STWDS) scheme. Moreover, temporally adjacent sources are likely to share discriminative information on object/non-object classification. In order to use such information, an adaptive SVM learning scheme is designed to transfer discriminative information across sources. Finally, the corresponding Dempster-Shafer belief function of the STWDS scheme is embedded into a Bayesian tracking model. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracking approach.Xi Li, Anthony Dick, Chunhua Shen, Zhongfei Zhang, Anton van den Hengel, Hanzi Wan

Incremental learning of 3D-DCT compact representations for robust visual tracking

Visual tracking usually requires an object appearance model that is robust to changing illumination, pose and other factors encountered in video. Many recent trackers utilize appearance samples in previous frames to form the bases upon which the object appearance model is built. This approach has the following limitations: (a) the bases are data driven, so they can be easily corrupted; and (b) it is difficult to robustly update the bases in challenging situations. In this paper, we construct an appearance model using the 3D discrete cosine transform (3D-DCT). The 3D-DCT is based on a set of cosine basis functions, which are determined by the dimensions of the 3D signal and thus independent of the input video data. In addition, the 3D-DCT can generate a compact energy spectrum whose high-frequency coefficients are sparse if the appearance samples are similar. By discarding these high-frequency coefficients, we simultaneously obtain a compact 3D-DCT based object representation and a signal reconstruction-based similarity measure (reflecting the information loss from signal reconstruction). To efficiently update the object representation, we propose an incremental 3D-DCT algorithm, which decomposes the 3D-DCT into successive operations of the 2D discrete cosine transform (2D-DCT) and 1D discrete cosine transform (1D-DCT) on the input video data. As a result, the incremental 3D-DCT algorithm only needs to compute the 2D-DCT for newly added frames as well as the 1D-DCT along the third dimension, which significantly reduces the computational complexity. Based on this incremental 3D-DCT algorithm, we design a discriminative criterion to evaluate the likelihood of a test sample belonging to the foreground object. We then embed the discriminative criterion into a particle filtering framework for object state inference over time. Experimental results demonstrate the effectiveness and robustness of the proposed tracker.Xi Li, Anthony Dick, Chunhua Shen, Anton van den Hengel, and Hanzi Wan

Performance evaluation for tracker-level fusion in video tracking

PhDTracker-level fusion for video tracking combines outputs (state estimations) from multiple trackers, to address the shortcomings of individual trackers. Furthermore, performance evaluation of trackers at run time (online) can determine low performing trackers that can be removed from the fusion. This thesis presents a tracker-level fusion framework that performs online tracking performance evaluation for fusion. We first introduce a method to determine time instants of tracker failure that is divided into two steps. First, we evaluate tracking performance by comparing the distributions of the tracker state and a region around the state. We use Distribution Fields to generate the distributions of both regions and compute a tracking performance score by comparing the distributions using the L1 distance. Then, we model this score as a time series and employ the Auto Regressive Moving Average method to forecast future values of the performance score. A difference between the original and forecast returns the forecast error signal that we use to detect tracking failure. We test the method with different datasets and then demonstrate its flexibility using tracking results and sequences from the Visual Object Tracking (VOT) challenge. The second part presents a tracker-level fusion method that combines the outputs of multiple trackers. The method is divided into three steps. First, we group trackers into clusters based on the spatio-temporal pair-wise relationships of their outputs. Then, we evaluate tracking performance based on reverse-time analysis with an adaptive reference frame and define the cluster with trackers that appear to be successfully following the target as the on-target cluster. Finally, we fuse the outputs of the trackers in the on-target cluster to obtain the final target state. The fusion approach uses standard tracker outputs and can therefore combine various types of trackers. We test the method with several combinations of state-of-the-art trackers, and also compare it with individual trackers and other fusion approaches.EACEA, under the EMJD ICE Project

Key-point based tracking for illegally parked vehicle detection

This research aims to develop a target detection and tracking system that can realize real-time video surveillance. The purpose of the research is to realize a monitoring application that can run automatically and intelligently to detect and track illegally parked vehicles. Since the application scenario of the algorithm is a real traffic environment, it must be able to adapt to complex environmental interference, such as drastic changes in lighting conditions, frequent occlusion, and long-term stable tracking. The thesis shows the detailed design process and test results of the system. This algorithm combines the target detection function based on deep learning network and the multi-object tracking algorithm based on key point matching. The method shown in the thesis focuses on detecting and tracking stationary vehicles in the no parking area. An object detection algorithm based on a deep learning network is used to recognize vehicles. Once the recognized vehicle is defined as an illegally parked vehicle through the determination of its motion state and location, an algorithm based on key-point matching is developed and tracked for this type of vehicle. If the target is still stationary in the no parking area after a period, the system will generate an alarm. The method was tested in more than 20 hours of video. The video comes from public database and our own. They all show real surveillance scenes, including different time periods of the day and different locations. The test results show that the method achieves 100% in precision (also called positive predictive value), 95% in recall (also known as sensitivity) and 97% in F1 (a measure that combines precision and recall). The results obtained also produce better detection and tracking compared to other comparable methods

BTLD+:A BAYESIAN APPROACH TO TRACKING LEARNING DETECTION BY PARTS

The contribution proposed in this thesis focuses on this particular instance of the visual tracking problem, referred as Adaptive Ap- iv \ufffcpearance Tracking. We proposed different approaches based on the Tracking Learning Detection (TLD) decomposition proposed in [55]. TLD decomposes visual tracking into three components, namely the tracker, the learner and detector. The tracker and the detector are two competitive processes for target localization based on comple- mentary sources of informations. The former searches for local fea- tures between consecutive frames in order to localize the target; the latter exploits an on-line appearance model to detect confident hy- pothesis over the entire image. The learner selects the final solution among the provided hypothesis. It updates the target appearance model, if necessary, reinitialize the tracker and bootstraps the detec- tor\u2019s appearance model. In particular, we investigated different ap- proaches to enforce the TLD stability. First, we replaced the tracker component with a novel one based on mcmc particle filtering; after- wards, we proposed a robust appearance modeling component able to characterize deformable objects in static images; after all, we inte- grated a modeling component able to integrate local visual features learning into the whole approach, lying to a couple layered represen- tation of the target appearance

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application