Delta Descriptors: Change-Based Place Representation for Robust Visual Localization

Visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheric conditions. In recent years a large range of approaches have been developed to address this challenge including deep-learnt image descriptors, domain translation, and sequential filtering, all with shortcomings including generality and velocity-sensitivity. In this paper we propose a novel descriptor derived from tracking changes in any learned global descriptor over time, dubbed Delta Descriptors. Delta Descriptors mitigate the offsets induced in the original descriptor matching space in an unsupervised manner by considering temporal differences across places observed along a route. Like all other approaches, Delta Descriptors have a shortcoming - volatility on a frame to frame basis - which can be overcome by combining them with sequential filtering methods. Using two benchmark datasets, we first demonstrate the high performance of Delta Descriptors in isolation, before showing new state-of-the-art performance when combined with sequence-based matching. We also present results demonstrating the approach working with four different underlying descriptor types, and two other beneficial properties of Delta Descriptors in comparison to existing techniques: their increased inherent robustness to variations in camera motion and a reduced rate of performance degradation as dimensional reduction is applied. Source code is made available at https://github.com/oravus/DeltaDescriptors.Comment: 8 pages and 7 figures. Published in 2020 IEEE Robotics and Automation Letters (RA-L

Non-Linear Temporal Subspace Representations for Activity Recognition

Representations that can compactly and effectively capture the temporal evolution of semantic content are important to computer vision and machine learning algorithms that operate on multi-variate time-series data. We investigate such representations motivated by the task of human action recognition. Here each data instance is encoded by a multivariate feature (such as via a deep CNN) where action dynamics are characterized by their variations in time. As these features are often non-linear, we propose a novel pooling method, kernelized rank pooling, that represents a given sequence compactly as the pre-image of the parameters of a hyperplane in a reproducing kernel Hilbert space, projections of data onto which captures their temporal order. We develop this idea further and show that such a pooling scheme can be cast as an order-constrained kernelized PCA objective. We then propose to use the parameters of a kernelized low-rank feature subspace as the representation of the sequences. We cast our formulation as an optimization problem on generalized Grassmann manifolds and then solve it efficiently using Riemannian optimization techniques. We present experiments on several action recognition datasets using diverse feature modalities and demonstrate state-of-the-art results.Comment: Accepted at the IEEE International Conference on Computer Vision and Pattern Recognition, CVPR, 2018. arXiv admin note: substantial text overlap with arXiv:1705.0858

Modelling of interactions for the recognition of activities in groups of people

In this research study we adopt a probabilistic modelling of interactions in groups of people, using video sequences, leading to the recognition of their activities. Firstly, we model short smooth streams of localised movement. Afterwards, we partition the scene in regions of distinct movement, by using maximum a posteriori estimation, by fitting Gaussian Mixture Models (GMM) to the movement statistics. Interactions between moving regions are modelled using the Kullback–Leibler (KL) divergence between pairs of statistical representations of moving regions. Such interactions are considered with respect to the relative movement, moving region location and relative size, as well as to the dynamics of the movement and location inter-dependencies, respectively. The proposed methodology is assessed on two different data sets showing different categories of human interactions and group activities

Human and Group Activity Recognition from Video Sequences

A good solution to human activity recognition enables the creation of a wide variety of useful applications such as applications in visual surveillance, vision-based Human-Computer-Interaction (HCI) and gesture recognition. In this thesis, a graph based approach to human activity recognition is proposed which models spatio-temporal features as contextual space-time graphs. In this method, spatio-temporal gradient cuboids were extracted at significant regions of activity, and feature graphs (gradient, space-time, local neighbours, immediate neighbours) are constructed using the similarity matrix. The Laplacian representation of the graph is utilised to reduce the computational complexity and to allow the use of traditional statistical classifiers. A second methodology is proposed to detect and localise abnormal activities in crowded scenes. This approach has two stages: training and identification. During the training stage, specific human activities are identified and characterised by employing modelling of medium-term movement flow through streaklines. Each streakline is formed by multiple optical flow vectors that represent and track locally the movement in the scene. A dictionary of activities is recorded for a given scene during the training stage. During the testing stage, the consistency of each observed activity with those from the dictionary is verified using the Kullback-Leibler (KL) divergence. The anomaly detection of the proposed methodology is compared to state of the art, producing state of the art results for localising anomalous activities. Finally, we propose an automatic group activity recognition approach by modelling the interdependencies of group activity features over time. We propose to model the group interdependences in both motion and location spaces. These spaces are extended to time-space and time-movement spaces and modelled using Kernel Density Estimation (KDE). The recognition performance of the proposed methodology shows an improvement in recognition performance over state of the art results on group activity datasets