A study on task difficulty and acceleration stress

The results of two experiments which relate to task difficulty and the effects of environmental stress on tracking performance are discussed and compared to subjective evaluations. The first experiment involved five different sum of sine tracking tasks which humans tracked both in a static condition and under a 5 Gz acceleration stress condition. The second experiment involved similar environmental stress conditions but in this case the tasks were constructed from deterministic functions with specially designed velocity and acceleration profiles. Phase Plane performance analysis was conducted to study potential measures of workload or tracking difficulty

A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects

Tracking humans that are interacting with the other subjects or environment remains unsolved in visual tracking, because the visibility of the human of interests in videos is unknown and might vary over time. In particular, it is still difficult for state-of-the-art human trackers to recover complete human trajectories in crowded scenes with frequent human interactions. In this work, we consider the visibility status of a subject as a fluent variable, whose change is mostly attributed to the subject's interaction with the surrounding, e.g., crossing behind another object, entering a building, or getting into a vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the causal-effect relations between an object's visibility fluent and its activities, and develop a probabilistic graph model to jointly reason the visibility fluent change (e.g., from visible to invisible) and track humans in videos. We formulate this joint task as an iterative search of a feasible causal graph structure that enables fast search algorithm, e.g., dynamic programming method. We apply the proposed method on challenging video sequences to evaluate its capabilities of estimating visibility fluent changes of subjects and tracking subjects of interests over time. Results with comparisons demonstrate that our method outperforms the alternative trackers and can recover complete trajectories of humans in complicated scenarios with frequent human interactions.Comment: accepted by CVPR 201

KONTROL ADAPTIF PADA MODEL PENYEBARAN KOLERA DENGAN ADANYA KETIDAKPASTIAN PARAMETER

In this paper the number of  humans infected with cholera was controlled under the uncertainty in cholera model parameters. The aim of this research is to design an adaptive control so that the number of infected humans decreases. To achieve this goal, an adaptive controller was proposed to a deterministic model for the transmission of cholera involving five state variables (susceptible humans, infected humans, quarantined humans, recovered humans, and bacterial concentration) and one input control variable, i.e, the proportion of quarantined humans. A control law was designed such that the number of infected humans was decreased tracking the given reference function. The tracking error convergence were analyzed by employing the  Lyapunov theorem. The performance of the proposed controller was evaluated through numerical simulations. The results show that the adaptive controller designed to the model ensures the tracking error convergence such that the number of infected humans has declined

Multisensor data fusion for joint people tracking and identification with a service robot

Tracking and recognizing people are essential skills modern service robots have to be provided with. The two tasks are generally performed independently, using ad-hoc solutions that first estimate the location of humans and then proceed with their identification. The solution presented in this paper, instead, is a general framework for tracking and recognizing people simultaneously with a mobile robot, where the estimates of the human location and identity are fused using probabilistic techniques. Our approach takes inspiration from recent implementations of joint tracking and classification, where the considered targets are mainly vehicles and aircrafts in military and civilian applications. We illustrate how people can be robustly tracked and recognized with a service robot using an improved histogram-based detection and multisensor data fusion. Some experiments in real challenging scenarios show the good performance of our solution

Humans in 4D: Reconstructing and Tracking Humans with Transformers

We present an approach to reconstruct humans and track them over time. At the core of our approach, we propose a fully "transformerized" version of a network for human mesh recovery. This network, HMR 2.0, advances the state of the art and shows the capability to analyze unusual poses that have in the past been difficult to reconstruct from single images. To analyze video, we use 3D reconstructions from HMR 2.0 as input to a tracking system that operates in 3D. This enables us to deal with multiple people and maintain identities through occlusion events. Our complete approach, 4DHumans, achieves state-of-the-art results for tracking people from monocular video. Furthermore, we demonstrate the effectiveness of HMR 2.0 on the downstream task of action recognition, achieving significant improvements over previous pose-based action recognition approaches. Our code and models are available on the project website: https://shubham-goel.github.io/4dhumans/.Comment: Project Webpage: https://shubham-goel.github.io/4dhumans

Visual and Proprioceptive Contributions to Compensatory and Pursuit Tracking Movements in Humans

An ongoing debate in the field of motor control considers how the brain uses sensory information to guide the formation of motor commands to regulate movement accuracy. Recent research has shown that the brain may use visual and proprioceptive information differently for stabilization of limb posture (compensatory movements) and for controlling goal-directed limb trajectory (pursuit movements). Using a series of five experiments and linear systems identification techniques, we modeled and estimated the sensorimotor control parameters that characterize the human motor response to kinematic performance errors during continuous compensatory and pursuit tracking tasks. Our findings further support the idea that pursuit and compensatory movements of the limbs are differentially controlled