Multi-Channel Features Spatio-Temporal Context Learning for Visual Tracking

Visual tracking is a challenging issue in surveillance, human-computer interaction and intelligent robotics, among others. Managing appearance changes of the target object, illumination changes, rotations, non-rigid deformations, partial or full occlusions, background clutter, fast motion, and so forth is generally difficult. Among the numerous existing trackers, the correlationfilter- based tracker can achieve appealing performance with a fast speed for fast Fourier transform (FFT). Motivated by this property, the spatio-temporal context (STC) learning algorithm was proposed with consideration of the information from the context around the target, and this algorithm achieved good results. However, STC only utilizes the overall intensity information. In this paper, we propose a multi-channel features spatio-temporal context (MFSTC) learning algorithm with an improved scaleadaptive scheme. Our algorithm integrates powerful features, including Histogram of Oriented Gradients (HoG) and color naming, using kernel methods on the basis of the STC algorithm to further enhance the overall tracking performance. Extensive experimental results obtained from various benchmark datasets demonstrate that the proposed tracker is promising for various challenging scenarios and maintains real-time performance at an average speed of 78 fps. According to the test results, our algorithm outperforms the STC algorithm and achieves performance that is competitive with the state-of-the-art algorithms

ModDrop: adaptive multi-modal gesture recognition

We present a method for gesture detection and localisation based on multi-scale and multi-modal deep learning. Each visual modality captures spatial information at a particular spatial scale (such as motion of the upper body or a hand), and the whole system operates at three temporal scales. Key to our technique is a training strategy which exploits: i) careful initialization of individual modalities; and ii) gradual fusion involving random dropping of separate channels (dubbed ModDrop) for learning cross-modality correlations while preserving uniqueness of each modality-specific representation. We present experiments on the ChaLearn 2014 Looking at People Challenge gesture recognition track, in which we placed first out of 17 teams. Fusing multiple modalities at several spatial and temporal scales leads to a significant increase in recognition rates, allowing the model to compensate for errors of the individual classifiers as well as noise in the separate channels. Futhermore, the proposed ModDrop training technique ensures robustness of the classifier to missing signals in one or several channels to produce meaningful predictions from any number of available modalities. In addition, we demonstrate the applicability of the proposed fusion scheme to modalities of arbitrary nature by experiments on the same dataset augmented with audio.Comment: 14 pages, 7 figure

Log-Euclidean Bag of Words for Human Action Recognition

Representing videos by densely extracted local space-time features has recently become a popular approach for analysing actions. In this paper, we tackle the problem of categorising human actions by devising Bag of Words (BoW) models based on covariance matrices of spatio-temporal features, with the features formed from histograms of optical flow. Since covariance matrices form a special type of Riemannian manifold, the space of Symmetric Positive Definite (SPD) matrices, non-Euclidean geometry should be taken into account while discriminating between covariance matrices. To this end, we propose to embed SPD manifolds to Euclidean spaces via a diffeomorphism and extend the BoW approach to its Riemannian version. The proposed BoW approach takes into account the manifold geometry of SPD matrices during the generation of the codebook and histograms. Experiments on challenging human action datasets show that the proposed method obtains notable improvements in discrimination accuracy, in comparison to several state-of-the-art methods

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table