Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence

There is growing interest in human activity recognition systems, motivated by their numerous promising applications in many domains. Despite much progress, most researchers have narrowed the problem towards fixed camera viewpoint owing to inherent difficulty to train their systems across all possible viewpoints. Fixed viewpoint systems are impractical in real scenarios. Therefore, we attempt to relax the fixed viewpoint assumption and present a novel and simple framework to recognize and classify human activities from uncalibrated monocular video source from any viewpoint. The proposed framework comprises two stages: 3D human pose estimation and human activity recognition. In the pose estimation stage, we estimate 3D human pose by a simple search-based and tracking-based technique. In the activity recognition stage, we use Nearest Neighbor, with Dynamic Time Warping as a distance measure, to classify multivariate time series which emanate from streams of pose vectors from multiple video frames. We have performed some experiments to evaluate the accuracy of the two stages separately. The encouraging experimental results demonstrate the effectiveness of our framework

Evolving cooperation in the spatial N-player snowdrift game

The Snowdrift game is a well-known social dilemma model frequently used in evolutionary game theory to investigate the emergence of cooperative behaviour under different biologically or socially plausible conditions. In this paper, we examine a multi-player version of the Snowdrift game where (i) the agents playing the game are mapped to the nodes of a regular two-dimensional lattice, (ii) the number of rounds of the game varies from a “one-shot” version to a fixed number of repeated interactions, and (iii) a genetic algorithm is used to evolve agent actions (strategy update) over a fixed number of generations. Comprehensive Monte Carlo simulation experiments show that cooperative behaviour is promoted in the multi-player iterated Snowdrift game. This emergent behaviour may be attributed to the combination of spatial reciprocity, based on the inherent capabilities of the genetic algorithm to explore the diverse sets of agents’ strategies, and repeated interactions. The simulation results also uncover some interesting findings regarding the effect of repeated interactions in the game

A multi-agent based migration model for evolving cooperation in the spatial N-player snowdrift game

In recent years, there has been an increased interest in using agent-based simulation models to investigate the evolution of cooperative behaviour in spatial evolutionary games. However, the relationship between individual player mobility (or migration) and population dynamics is not clear. In this paper, we investigate the impacts of alternative migration mechanisms in the spatial N-player Snowdrift game. Here, agents occupy sites in a two-dimensional toroidal lattice. Specific game instances are created by nominating N sites from each of the local neighbourhoods. We use a genetic algorithm to evolve agent game-playing strategies. In addition, agents have an opportunity to migrate to different sites in the lattice at regular intervals. Key parameters in our model include the migration rate, the actual dispersal distance, the "take-over" scheme, the group size N, and the relative cost-to-benefit ratio of the game. Detailed simulation experiments show that the proposed model is able to promote cooperation in a population of mobile agents. However, the magnitude of the dispersal distance plays a significant role in determining population dynamics. Our findings help to further understand how migratory (mobility) patterns affect evolutionary processes