Fahimeh Rezazadegan

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Deep learning models have achieved state-of-the-art performance in recognizing human activities, but often rely on utilizing background cues present in typical computer vision datasets that predominantly have a stationary camera. If these models are to be employed by autonomous robots in real world environments, they must be adapted to perform independently(More)
Object detection is a fundamental task in many computer vision applications, therefore the importance of evaluating the quality of object detection is well acknowledged in this domain. This process gives insight into the capabilities of methods in handling environmental changes. In this paper, a new method for object detection is introduced that combines(More)
The paper addresses the problem of trajectory tracking control of underactuated underwater vehicles in the presence of parametric uncertainty. Based on backstepping design approach, an adaptive control law for 6 DOF model is derived for the trajectory tracking problem. The desired trajectory does not need to be of a particular type and in fact can be any(More)
Anticipating future actions is a key component of intelligence, specifically when it applies to real-time systems, such as robots or autonomous cars. While recent work has addressed prediction of raw RGB pixel values in future video frames, we focus on predicting further in future by predicting a summary of moving pixels through a sequence of frames which(More)
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