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

Influence of self-disassembly of bridges on collective flow characteristics of swarm robots in a single-lane and periodic system with a gap

Inspired by the living bridges formed by ants, swarm robots have been developed to self-assemble bridges to span gaps and self-disassemble them. Self-disassembly of bridges may increase the transport efficiency of swarm robots by increasing the number of moving robots, and also may decrease the efficiency by causing gaps to reappear. Our aim is to elucidate the influence of self-disassembly of bridges on the collective flow characteristics of swarm robots in a single-lane and periodic system with a gap. In the system, robots span and cross the gap by self-assembling a single-layer bridge. We consider two scenarios in which self-disassembling bridges is prevented (prevent-scenario) or allowed (allow-scenario). We represent the horizontal movement of robots with a typical car-following model, and simply model the actions of robots for self-assembling and self-disassembling bridges. Numerical simulations have revealed the following results. Flow-density diagrams in both the scenarios shift to the higher-density region as the gap length increases. When density is low, allow-scenario exhibits the steady state of repeated self-assembly and self-disassembly of bridges. If density is extremely low, flow in this state is greater than flow in prevent-scenario owing to the increase in the number of robots moving horizontally. Otherwise, flow in this state is smaller than flow in prevent-scenario. Besides, flow in this state increases monotonically with respect to the velocity of robots in joining and leaving bridges. Thus, self-disassembling bridges is recommended for only extremely low-density conditions in periodic systems. This study contributes to the development of the collective dynamics of self-driven particles that self-assemble structures, and stirs the dynamics with other self-assembled structures, such as ramps, chains, and towers.Comment: 13 pages, 9 figure

A general architecture for robotic swarms

Swarms are large groups of simplistic individuals that collectively solve disproportionately complex tasks. Individual swarm agents are limited in perception, mechanically simple, have no global knowledge and are cheap, disposable and fallible. They rely exclusively on local observations and local communications. A swarm has no centralised control. These features are typifed by eusocial insects such as ants and termites, who construct nests, forage and build complex societies comprised of primitive agents. This project created the basis of a general swarm architecture for the control of insect-like robots. The Swarm Architecture is inspired by threshold models of insect behaviour and attempts to capture the salient features of the hive in a closely defined computer program that is hardware agnostic, swarm size indifferent and intended to be applicable to a wide range of swarm tasks. This was achieved by exploiting the inherent limitations of swarm agents. Individual insects were modelled as a machine capable only of perception, locomotion and manipulation. This approximation reduced behaviour primitives to a fixed tractable number and abstracted sensor interpretation. Cooperation was achieved through stigmergy and decisions made via a behaviour threshold model. The Architecture represents an advance on previous robotic swarms in its generality - swarm control software has often been tied to one task and robot configuration. The Architecture's exclusive focus on swarms, sets it apart from existing general cooperative systems, which are not usually explicitly swarm orientated. The Architecture was implemented successfully on both simulated and real-world swarms