Maziar Palhang

Mohamad Mahdi Kassir, Maziar Palhang, Mohammad Reza Ahmadzadeh

Funnel lane concept is a qualitative visual navigation method which helps robots to autonomously navigate by using a recorded video. A visual path is extracted from the video by extracting some keyframes from the video. The robot uses this visual path for its navigation. Funnel lane unlike some other methods does not make use of traditional calculations of Jacobians, homographies, fundamental matrices, or the focus of expansion, and does not require any camera calibration. However, funnel lane has some shortcomings. One problem is that funnel lane gives no information about the radius of rotation, so in turnings, the robot turns by a constant radius of rotation along the path. This reduces the maneuverability and limits the robot from dealing with all turnings conditions. In addition, this problem makes the robot faces a serious problem in correcting its path when it deviates from the desired path. Another flaw is that in some situations the robot faces an ambiguity to understand whether a translation or a rotation should be followed in the visual path which leads the robot to deviate and to fail in following the desired path. This paper introduces the sloped funnel lane technique which does not have these shortcomings. The roll and pitch angles are added to the funnel lane, which help the robot to set its radius of rotation according to the turnings conditions it faces. Moreover, they help to reduce the ambiguity between translation and rotation. Therefore the robot can deal with different turnings conditions and the navigation method will be more robust and accurate. Experimental results on challenging scenarios on a real ground robot demonstrate the effectiveness of sloped funnel lane technique.

Masoud Faraki, Maziar Palhang, Conrad Sanderson

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.