Feature point tracking combining the Interacting Multiple Model filter and an efficient assignment algorithm
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Object classification and detection aim at recognizing and localizing objects in real-world images. They are fundamental computer vision problems and a prerequisite for full scene understanding. Their difficulty lies in the large number of possible object ...
Programme doctoral en Informatique, Communications et Information2013
Amplitude and frequency are the two primary features of one-dimensional signals, and thus both are widely utilized to analysis data in numerous fields. While amplitude can be examined directly, frequency requires more elaborate approaches, except in the si ...
In very high resolution (VHR) image classification it is common to use spatial filters to enhance the discrimination among landuses related to similar spectral properties but different spatial characteristics. However, the filters types that can be used ar ...
The paper describes the development and the performance assessment of a Hardware-in-the-Loop (HIL) test platform built as a proof-of-concept of a sub-second State Estimator (SE) of Active Distribution Networks (ADNs). The SE relies on the availability of d ...
Cigré - Conseil International des Grands Réseaux Électriques2013
This paper introduces a state estimation framework for legged robots that allows estimating the full pose of the robot without making any assumptions about the geometrical structure of its environment. This is achieved by means of an Observability Constrai ...
Edge-preserving smoothers need not be taxed by a severe computational cost. We present, in this paper, a lean algorithm that is inspired by the bi-exponential filter and preserves its structure-a pair of one-tap recursions. By a careful but simple local ad ...
Institute of Electrical and Electronics Engineers2012
We consider the estimation of multiple time-domain sparse filters from echoic mixtures of several unknown sources, when the sources are sparse in the time-frequency domain. We propose a sparse filter estimation framework consisting of two steps: a) a clust ...
This paper presents a new approach to measuring similarity over massive time-series data. Our approach is built on two principles: one is to parallelize the large amount computation using a scalable cloud serving system, called TimeCloud. The another is to ...
In this work, we address the problem of tracking an acoustic source based on measured time differences of arrival (TDOA). The classical solution to this problem consists in using a detector, which estimates the TDOA for each microphone pair, and then apply ...
We build a real-time multi-people tracker, which is based on the Kalman Filter. The input to the software is a Probabilistic Occupancy Map of the observed area. The main goal of the project is to incorporate this tracker to the real-time detection software ...