Subhash Challa

University of Melbourne, Architecture, Building, and Planning, Adjunct

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Papers by Subhash Challa

Fundamentals of Object Tracking: References

Fundamentals of Object Tracking: Preface

Review Autonomous Vehicles Navigation with Visual Target Tracking: Technical Approaches

This paper surveys the developments of last 10 years in the area of vision based target tracking ... more This paper surveys the developments of last 10 years in the area of vision based target tracking for autonomous vehicles navigation. First, the motivations and applications of using vision based target tracking for autonomous vehicles navigation are presented in the introduction section. It can be concluded that it is very necessary to develop robust visual target tracking based navigation algorithms for the broad applications of autonomous vehicles. Then this paper reviews the recent techniques in three different categories: vision based target tracking for the applications of land, underwater and aerial vehicles navigation. Next, the increasing trends of using data fusion for visual target tracking based autonomous vehicles navigation are discussed. Through data fusion the tracking performance is improved and becomes more robust. Based on the review, the remaining research challenges are summarized and future research directions are investigated.

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Fundamentals of Object Tracking: References

Opportunistic information fusion: a new paradigm for next generation networked sensing systems

2005 7th International Conference on Information Fusion, 2005

Traditionally, Information Fusion systems assume that the information is gathered from known sens... more Traditionally, Information Fusion systems assume that the information is gathered from known sensors over proprietary communication networks and fuse using fixed rules of information fusion and designated computing and communication resources. Emerging technologies like wireless sensor networks, TEDS enabled legacy sensors, ubiquitous computing devices and all IP next generation networks are challenging the rationale of conventional information fusion systems. Tae technology has matured to a point where it is reasonable to discover sensors based on the context, establish relevance, queryfor appropriate data, andfuse it using the most appropriatefusion rule, using ubiquitous computing and communication environment in an opportunistic manner. We define such fusion systems as opportunistic information fusion systems. In this paper we introduce this new paradigm for information fusion and identify plausible approaches and challenges to design, develop and deploy the proposed next generation opportunistic information fusion systems.

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Data fusion techniques for auto calibration in wireless sensor networks

Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of intere... more Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of interest. Sensor measurements in sensor networks usually suffer from both random errors (noise) and systematic errors (drift and bias). Even when the sensors are properly calibrated at the time of deployment, they develop errors in their readings leading to erroneous inferences to be made by the network. In this paper we present a novel algorithm for detecting and correcting sensor measurement errors by utilising the spatio-temporal correlation among the neighbouring sensors. The algorithm is designed for sparsely deployed wireless sensor networks. It can follow and correct both slowly and suddenly changing sensor measurements. As a result, the algorithm can adapt for under sampling the sensor measurements. Therefore, it allows for reducing the communication between sensors to maintain the calibration which leads to reducing the energy consumed from the batteries. The algorithm runs recursivel...

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Motion based 3D Target Tracking with Interacting Multiple Linear Dynamic Models

In this paper, an algorithm is proposed for vision-based object identification and tracking by au... more In this paper, an algorithm is proposed for vision-based object identification and tracking by autonomous vehicles. In order to estimate the speed of the tracking object, this algorithm fuses information captured by on-board sensors such as camera and inertial sensors. To formulate the tracking algorithm it is necessary to use a proper model which describes the dynamics of the tracking object. However due to complex nature of the moving object, it is necessary to have different dynamic models. Here, several simple and basic linear dynamic models are combined to approximate unpredictable, complex dynamics of the moving target. With these basic linear dynamic models, a detailed description of the three dimensional (3D) target tracking scheme using an interacting multiple model (IMM)alongwithanExtendedKalmanFilteringispresented. Thefinal state of the target is estimated as a weighted combination of the outputs from each different dynamic model. Performance of the proposed interacting m...

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Extraction and Scheduling in Heterogeneous Sensor Networks

Determining the output of the most relevant sensor is of crucial importance when heterogeneous se... more Determining the output of the most relevant sensor is of crucial importance when heterogeneous sensors are available for measuring a given process in an environment. In this paper, we describe an IEEE 1451 TEDS (Transducer Electronic Data Sheets) compliant sensor model for heterogeneous sensor networks. The proposed model uses the relevance feed back method to understand the context of a sensor learning application. We present results of a real time implementation of heterogeneous sensor networks using distributed multi-sensing 3D Real-time Robotics Software Player/Gazebo on an autonomous mobile robot’s navigation problem. The results show that the proposed model can be utilised in the real-time scenario and can help reduce the computational cost of a system.

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Decentralised Fusion of Disparate Identity Estimates for Shared Situation Awareness

Network-centric warfare (NCW) and the interoperability of joint and coalition forces lie among th... more Network-centric warfare (NCW) and the interoperability of joint and coalition forces lie among the future warfighting concepts that have been identified by defence. The purpose behind the introduction of such concepts is to “link sensors, engagement systems and decision-makers into an effective and responsive whole, through shared situation awareness, clear procedures and the information connectivity needed to synchronise the actions of the defence force to meet the commander’s intent [1].” To realise the goal of shared situation awareness for NCW, it has long been acknowledged that decentralised data fusion is a key enabling technology, and to this end it has been investigated in terms of distributed target tracking and identification, and the development of distributed agents and ontologies. However, the aspects of interoperability relating to the fusion of disparate types of uncertain (local) data from joint and coalition data fusion systems for shared situation awareness do not ...

Information, Decision and Control

Multiple Pedestrian Tracking From Monocular Videos in an Interacting Multiple Model Framework

IEEE Transactions on Image Processing

Heterogeneous fusion with a combined evidential, probability and OWA methods for target classification

Fusing multiple independent sensor measurements and human intelligence reports are essential to s... more Fusing multiple independent sensor measurements and human intelligence reports are essential to support critical decisions in a timely manner for today's situation awareness systems. The problem of great significance is associated with fusing Human Originated Information (HOI) with the information from other sources. Ordered Weighted Average (OWA) algorithm was proposed recently as a means to assimilate uncertain human originated information.

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1999 Brest Joint Target Tracking and Classification

Comments on " Joint Target Tracking and Classification Using Radar and ESM Sensors " - Authors' Reply

Ieee Transactions on Aerospace and Electronic Systems, Apr 1, 2004

ABSTRACT For original paper see Challa and Pulford (2001). Joint target tracking and classificati... more ABSTRACT For original paper see Challa and Pulford (2001). Joint target tracking and classification (JTC) filter was presented in the above mentioned paper. A comment is made here to point out that JTC filter has no advantage over interacting multiple model (IMM) filter from a theoretical viewpoint.

Multitarget tracking algorithm - Joint IPDA and Gaussian mixture PHD filter

2009 12th International Conference on Information Fusion, Jul 6, 2009

Abstract Random Finite Set approach is a mathemat-ically rigorous framework for multi-target tr... more

Vision based data fusion for autonomous vehicles target tracking using interacting multiple dynamic models

Computer Vision and Image Understanding, 2008

In this paper, a novel algorithm is proposed for the vision-based object tracking by autonomous v... more In this paper, a novel algorithm is proposed for the vision-based object tracking by autonomous vehicles. To estimate the velocity of the tracked object, the algorithm fuses the information captured by the vehicle's on-board sensors such as the cameras and inertial motion sensors. Optical flow vectors, color features, stereo pair disparities are used as optical features while the vehicle's inertial measurements are used to determine the cameras' motion. The algorithm determines the velocity and position of the target in the world coordinate which are then tracked by the vehicle. In order to formulate this tracking algorithm, it is necessary to use a proper model which describes the dynamic information of the tracked object. However due to the complex nature of the moving object, it is necessary to have robust and adaptive dynamic models. Here, several simple and basic linear dynamic models are selected and combined to approximate the unpredictable, complex or highly nonlinear dynamic properties of the moving target. With these basic linear dynamic models, a detailed description of the three-dimensional (3D) target tracking scheme using the Interacting Multiple Models (IMM) along with an Extended Kalman Filter is presented. The final state of the target is estimated as a weighted combination of the outputs from each different dynamic model. Performance of the proposed fusion based IMM tracking algorithm is demonstrated through extensive experimental results.

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Online drift correction in wireless sensor networks using spatio-temporal modeling

2008 11th International Conference on Information Fusion, 2008

Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of intere... more Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of interest. Sensors in the sensor network can suffer from both random and systematic bias problems. Even when the sensors are properly calibrated at the time of their deployment, they develop drift in their readings leading to erroneous inferences being made by the network. The drift in this context is defined as a slow, unidirectional, long-term change in the sensor measurements. In this paper we present a novel algorithm for detecting and correcting sensors drifts by utilising the spatio-temporal correlation between neigbouring sensors. Based on the assumption that neighbouring sensors have correlated measurements and that the instantiation of drift in a sensor is uncorrelated with other sensors, each sensor runs a support vector regression algorithm on its neigbours' corrected readings to obtain a predicted value for its measurements. It then uses this predicted data to self-assess its measurement and detect and correct its drift using a Kalman filter. The algorithm is run recursively and is totally decentralized. We demonstrate using real data obtained from the Intel Berkeley Laboratory that our algorithm successfully suppresses drifts developed in sensors and thereby prolongs the effective lifetime of the network.

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Vision Based Target Tracking for Autonomous Land Vehicle Navigation: A Brief Survey

Recent Patents on Computer Science, 2010

... the outliers in the scene contribute false measurements for estimation, they introduce a ster... more

Non-linear Filtering Using Probability Density Evolutions

Isspa 96 Fourth International Symposium on Signal Processing and Its Applications Proceedings Vols 1 and 2, 1996

The solution of the Fokker-Planck-Kolmogorov (FPK) forward diffusion equation in conjunction with... more The solution of the Fokker-Planck-Kolmogorov (FPK) forward diffusion equation in conjunction with Bayes’ conditional density lemma provides optimal (minimum variance) state estimates of any general stochastic dynamic system (SDS). It has been well documented in non-linear filtering literature that the analytical solution for the FPK equation is extremely difficult to obtain except in a few special cases. In this paper we propose the use of numerical solution of FPK to obtain the optimal state estimates of a non-linear dynamic system. The proposed method provides the conditional densities &om which the conditional means (the optimal state estimates) can be easily evaluated. The estimated conditional densities clearly violate the assump tions of Gaussianity implicitly required by Extended Kalman Filter (EKF) based approaches. The performance of the proposed method is compared with that of the EKF. Monte-Carlo simulation results are provided to show the superior performance of the density evolution method.

A study of the application of particle filters to single target tracking problems

Spie Proceedings Series, 2003

Subhash Challa

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