Learning detectors quickly using structured covariance matrices

Computer vision is increasingly becoming interested in the rapid estimation of object detectors. Canonical hard negative mining strategies are slow as they require multiple passes of the large negative training set. Recent work has demonstrated that if the distribution of negative examples is assumed to be stationary, then Linear Discriminant Analysis (LDA) can learn comparable detectors without ever revisiting the negative set. Even with this insight, however, the time to learn a single object detector can still be on the order of tens of seconds on a modern desktop computer. This paper proposes to leverage the resulting structured covariance matrix to obtain detectors with identical performance in orders of magnitude less time and memory. We elucidate an important connection to the correlation filter literature, demonstrating that these can also be trained without ever revisiting the negative set

Staple: Complementary Learners for Real-Time Tracking

Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes. However, since the model that they learn depends strongly on the spatial layout of the tracked object, they are notoriously sensitive to deformation. Models based on colour statistics have complementary traits: they cope well with variation in shape, but suffer when illumination is not consistent throughout a sequence. Moreover, colour distributions alone can be insufficiently discriminative. In this paper, we show that a simple tracker combining complementary cues in a ridge regression framework can operate faster than 80 FPS and outperform not only all entries in the popular VOT14 competition, but also recent and far more sophisticated trackers according to multiple benchmarks.Comment: To appear in CVPR 201

Discriminative Scale Space Tracking

Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and struggles when encountered with large scale variations. This paper investigates the problem of accurate and robust scale estimation in a tracking-by-detection framework. We propose a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation. The explicit scale filter is learned online using the target appearance sampled at a set of different scales. Contrary to standard approaches, our method directly learns the appearance change induced by variations in the target scale. Additionally, we investigate strategies to reduce the computational cost of our approach. Extensive experiments are performed on the OTB and the VOT2014 datasets. Compared to the standard exhaustive scale search, our approach achieves a gain of 2.5% in average overlap precision on the OTB dataset. Additionally, our method is computationally efficient, operating at a 50% higher frame rate compared to the exhaustive scale search. Our method obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state-of-the-art trackers on VOT2014.Comment: To appear in TPAMI. This is the journal extension of the VOT2014-winning DSST tracking metho

Robust Visual Tracking Using Illumination Invariant Features in Adaptive Scale Model

When entering into the realm of Computer Vision, the first thing which comes in to mind is Visual tracking. Visual tracking by far comes into one of the most actively investigated research areas because of the fact that it has an extensive collection of applications in areas such as activity recognition, surveillance, motion analysis and as well as human computer interaction. Some serious challenges of this area which still create hindrance in achieving 100% accuracy are abrupt appearance and pose changes of an object along with its background blockage due to blockages called occlusion, illumination and lighting variances and changes in scale of target object in the frames. Moreover, diverse algorithms had been proposed for the resolution of said issue. Now in such cases, if we study the statistical analysis of correlation between two frames in a certain video, it can be efficiently utilized to get the most exact location of the targeted object. The algorithms in existence today do not completely exploit a strong spatio-temporal relationship that very often occurs between the two successive frames in a video sequence. Recent advances in correlation-based tracking systems have been proposed to address the problem in successive frames. In this thesis a very simple yet quite speedy and robust algorithm that in actual brings all the relevant information used for Visual Tracking. Two of the Models proposed are the “Locality Sensitive Histogram” and “Discriminative Scale Tracking Method”. These are robust enough to the variations which are based on appearance which are normally presented by blockage, pose, illumination and lighting variations alike. A scheme is proposed called scale adaptation which is very much clever to adapt variations of targeted scale in the most efficient manner. The Discriminative Scale Tracking Method is used for detection as well as scale change ultimately resulting in an effective tracking method in the end. Various different experiments with the best algorithms have demonstrated on challenging sequences that the suggested methodology attains promising results as far as robustness, accuracy, and speed is concerned