End-to-end active object tracking via reinforcement learning hernia lumbar

We study active object tracking, where a tracker takes hernia de disco lumbar tratamiento as input the visual observation (i.e. frame sequence) and produces the camera control signal (e.g., move forward, turn left, etc). Conventional methods tackle the tracking and the camera control separately, which is challenging to tune jointly. It also incurs many human efforts dolor lumbar embarazo for labeling and many expensive trial-and-errors in real-world. To address these issues, we propose, in this paper, an end-to-end solution via deep reinforcement learning, where a ConvNet-LSTM function approximator is adopted for escoliosis levoconvexa the direct frame-to-action prediction. We further propose an environment augmentation technique and a customized reward function, which are crucial for a successful training.


The tracker trained in simulators (ViZDoom, Unreal Engine) shows good generalization hernia lumbar in the case of unseen object moving path, unseen object appearance, unseen background, and distracting object. It can restore tracking when occasionally losing the target. With the experiments over the VOT dataset dolor lumbar y pierna izquierda, we also find that the tracking ability, obtained solely from simulators, can potentially transfer to real-world scenarios.

abstract = {We study active object tracking, where a tracker takes as input the visual observation (i.e. frame sequence) and produces the camera control signal (e.g., move forward, turn left, etc). Conventional methods tackle the tracking and the camera control separately, which escoliosis dorsal de convexidad derecha is challenging to tune jointly. It also incurs many human hernia discal lumbar efforts for labeling and many expensive trial-and-errors in real-world. To address these issues, we propose, in this paper, an end-to-end solution via deep reinforcement learning, where a ConvNet-LSTM function approximator is adopted for the direct frame-to-action prediction. We further propose an environment augmentation technique and a customized reward function, which are crucial for a successful training. The tracker trained in simulators estenosis lumbar (ViZDoom, Unreal Engine) shows good generalization in the case of unseen object moving path, unseen object appearance, unseen background dolor lumbar derecho tratamiento, and distracting object. It can restore tracking when occasionally losing the target. With the experiments over the VOT dataset, we also find that the tracking ability, obtained solely from simulators, can potentially transfer to real-world scenarios.}

%X We study active object tracking, where a tracker takes as input the visual observation (i.e. frame sequence) and produces the dolor lumbar bajo camera control signal (e.g., move forward, turn left, etc). Conventional methods tackle the tracking and the camera control separately, which is challenging to tune jointly. It also incurs many human escoliosis lumbar leve efforts for labeling and many expensive trial-and-errors in real-world. To address these issues, we propose, in this paper, an end-to-end solution via deep reinforcement learning, where a ConvNet-LSTM function approximator is adopted for hernia lumbar ejercicios prohibidos the direct frame-to-action prediction. We further propose an environment augmentation technique and a customized reward function, which are crucial for a successful training. The tracker trained in simulators (ViZDoom, Unreal Engine) shows good generalization in the case of unseen object moving path, unseen ejercicios hernia discal lumbar object appearance, unseen background, and distracting object. It can restore tracking when occasionally losing the target. With the experiments over the VOT dolor lumbar izquierdo dataset, we also find that the tracking ability, obtained solely from simulators, can potentially transfer to real-world scenarios.