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Online object trajectory classification using FPGA-SoC devices
Real time classification of objects using computer vision techniques are becoming relevant with emergence of advanced perceptions systems required by, surveillance systems, industry 4.0 robotics and agricultural robots. Conventional video surveillance basically detects and tracks moving object whereas there is no indication of whether the object is approaching or receding the camera (looming). Looming detection and classification of object movements aids in knowing the position of the object and plays a crucial role in military, vehicle traffic management, robotics, etc. To accomplish real-time object trajectory classification, a contour tracking algorithm is necessary. In this paper, an application is made to perform looming detection and to detect imminent collision on a system-on-chip field-programmable gate array (SoC- FPGA) hardware. The work presented in this paper was designed for running in Robotic platforms, Unmanned Aerial Vehicles, Advanced Driver Assistance System, etc. Due to several advantages of SoC-FPGA the proposed work is performed on the hardware. The proposed work focusses on capturing images, processing, classifying the movements of the object and issues an imminent collision warning on-the-fly. This paper details the proposed software algorithm used for the classification of the movement of the object, simulation of the results and future work
Fast and reliable recognition of human motion from motion trajectories using wavelet analysis
Recognition of human motion provides hints to understand human activities and gives opportunities to the development of new human-computer interface. Recent studies, however, are limited to extracting motion history image and recognizing gesture or locomotion of human body parts. Although the approach employed, i.e. the transformation of the 3D space-time (x-y-t) analysis to the 2D image analysis, is faster than analyzing 3D motion feature, it is less accurate and less robust in nature. In this paper, a fast trajectory-classification algorithm for interpreting movement of human body parts using wavelet analysis is proposed to increase the accuracy and robustness of human motion recognition. By tracking human body in real time, the motion trajectory (x-y-t) can be extracted. The motion trajectory is then broken down into wavelets that form a set of wavelet features. Classification based on the wavelet features can then be done to interpret the human motion. An online hand drawing digit recognition system was built using the proposed algorithm. Experiments show that the proposed algorithm is able to recognize digits from human movement accurately in real time.postprintThe 2004 IFIP International Conference on Artificial Intelligence Applications and Innovation, Toulouse, France, 22-27 August 2004. In Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovation, 2004, p. 1-1
Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions
To plan safe trajectories in urban environments, autonomous vehicles must be
able to quickly assess the future intentions of dynamic agents. Pedestrians are
particularly challenging to model, as their motion patterns are often uncertain
and/or unknown a priori. This paper presents a novel changepoint detection and
clustering algorithm that, when coupled with offline unsupervised learning of a
Gaussian process mixture model (DPGP), enables quick detection of changes in
intent and online learning of motion patterns not seen in prior training data.
The resulting long-term movement predictions demonstrate improved accuracy
relative to offline learning alone, in terms of both intent and trajectory
prediction. By embedding these predictions within a chance-constrained motion
planner, trajectories which are probabilistically safe to pedestrian motions
can be identified in real-time. Hardware experiments demonstrate that this
approach can accurately predict pedestrian motion patterns from onboard
sensor/perception data and facilitate robust navigation within a dynamic
environment.Comment: Submitted to 2014 International Workshop on the Algorithmic
Foundations of Robotic
Autonomous real-time surveillance system with distributed IP cameras
An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image
processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects
moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator
Unravelling intermittent features in single particle trajectories by a local convex hull method
We propose a new model-free method to detect change points between distinct
phases in a single random trajectory of an intermittent stochastic process. The
local convex hull (LCH) is constructed for each trajectory point, while its
geometric properties (e.g., the diameter or the volume) are used as
discriminators between phases. The efficiency of the LCH method is validated
for six models of intermittent motion, including Brownian motion with different
diffusivities or drifts, fractional Brownian motion with different Hurst
exponents, and surface-mediated diffusion. We discuss potential applications of
the method for detection of active and passive phases in the intracellular
transport, temporal trapping or binding of diffusing molecules, alternating
bulk and surface diffusion, run and tumble (or search) phases in the motion of
bacteria and foraging animals, and instantaneous firing rates in neurons
Online Informative Path Planning for Active Classification on UAVs
We propose an informative path planning (IPP) algorithm for active
classification using an unmanned aerial vehicle (UAV), focusing on weed
detection in precision agriculture. We model the presence of weeds on farmland
using an occupancy grid and generate plans according to information-theoretic
objectives, enabling the UAV to gather data efficiently. We use a combination
of global viewpoint selection and evolutionary optimization to refine the UAV's
trajectory in continuous space while satisfying dynamic constraints. We
validate our approach in simulation by comparing against standard "lawnmower"
coverage, and study the effects of varying objectives and optimization
strategies. We plan to evaluate our algorithm on a real platform in the
immediate future.Comment: 7 pages, 4 figures, submission to International Symposium on
Experimental Robotics 201
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