Reza Hoseinnezhad

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This paper presents a novel Bayesian method to track multiple targets in an image sequence without explicit detection. Our method is formulated based on finite set representation of the multi-target state and the recently developed multi-Bernoulli filter. Experimental results on sport player and cell tracking studies show that our method can automatically(More)
This correspondence presents a novel method for simultaneous tracking of multiple non-stationary targets in video. Our method operates directly on the video data and does not require any detection. We propose a multi-target likelihood function for the background-subtracted grey-scale image data, which admits multi-target conjugate priors. This allows the(More)
In Bayesian multi-target filtering knowledge of parameters such as clutter intensity and detection probability profile are of critical importance. Significant mismatches in clutter and detection model parameters results in biased estimates. In this paper we propose a multi-target filtering solution that can accommodate non-linear target models and an(More)
Almost every single-view visual multi-target tracking method presented in the literature includes a detection routine that maps the image data to point measurements relevant to the target states. These measurements are commonly further processed by a filter to estimate the number of targets and their states. This paper presents a novel visual tracking(More)
A new method is presented for integration of audio and visual information in multiple target tracking applications. The proposed approach uses a Bayesian filtering formulation and exploits multi-Bernoulli random finite set approximations. The work presented in this paper is the first principled Bayesian estimation approach to solve the sensor fusion(More)
In this paper, we propose a distributed multiobject tracking algorithm through the use of multi-Bernoulli (MB) filter based on generalized covariance intersection (G-CI). Our analyses show that the G-CI fusion with two MB posterior distributions does not admit an accurate closed-form expression. To solve this problem, we first approximate the fused(More)
Resolvers are absolute angle transducers that are usually used for position and speed measurement in permanent magnet motors. An observer that uses the sinusoidal signals of the resolver for this measurement is called an Angle Tracking Observer (ATO). Current designs for such observers are not stable in high acceleration and high-speed applications. This(More)
Most robust estimators, designed to solve computer vision problems, use random sampling to optimize their objective functions. Since the random sampling process is patently blind and computationally cumbersome, other searches of parameter space using techniques such as Nelder Meade simplex or gradient search techniques have been also proposed (particularly(More)
Recent designs for brake-by-wire systems use "resolvers" to provide accurate and continuous measurements for the absolute position and speed of the rotor of the electric actuators in brake callipers (permanent magnet DC motors). Resolvers are absolute-angle transducers that are integrated with estimator modules called "angle tracking observer" and together(More)
The random set based multi-Bernoulli filter is applied to a challenging low signal to noise track before detect scenario. Specifically we use the variant of the multi-Bernoulli filter that processes raw image observations. We add an additional layer of track management logic to output trajectories rather than point estimates. The tracker also exploits(More)