![shotbot para gunz 2017 shotbot para gunz 2017](https://www.zonammorpg.com/wp-content/uploads/2013/07/Gunz2_Artwork-1620x600.jpg)
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To deal with this, we develop a fast CNN-based one-stage detector that benefits from complementary RGB and depth image data and regresses 3D human centroids in an end-to-end fashion. One insight we gain from initial experiments is that recent CNN-based detectors perform well on 2D image-based detection, but this does not easily translate into robust localization in 3D world space. Therefore, we focus our subsequent research on the detection task. Our key finding is that detector performance is the single, most influential factor affecting tracking performance which goes far beyond the impact of the chosen tracking algorithm. We find that our efficient baseline method outperforms all other evaluated methods on the MOTA metric across all settings. After automated hyperparameter optimization, we compare our method systematically under different detector combinations to a hypothesis-oriented MHT, a track-oriented MDL tracker, and different NN variants on two novel datasets. Here, we first introduce a computationally very efficient tracking baseline: Using a relatively cheap set of extensions from the target tracking community to systematically tackle shortcomings of current systems, we attempt to improve robustness without resorting to more complex data association methods. We then take a step back from group tracking and investigate the problem of tracking individual humans in crowded scenes using a mobile platform with a multi-modal sensor setup. We apply the method to socially-aware navigation use-cases and present further experiments on simulated data in a more crowded environment, where we examine limitations of the hypothesis-oriented MHT approach under real-time constraints. In qualitative experiments on a novel dataset from a pedestrian zone, we achieve good real-time tracking performance for varying group sizes with few identifier switches. To this end, we address the problem of joint individual-group tracking using learned pairwise social relations in RGB-D by extending an existing multi-model multi-hypothesis tracking method with a mechanism to maintain consistent group identities. We start this thesis with the question if complex data association methods are suitable for tracking groups of people in general, and in crowded environments in particular. To address this problem, we examine both classical, model-based approaches and deep learning-based methods, and evaluate them on novel datasets as well as during real-world deployments on different mobile robot platforms in populated indoor scenarios. This thesis deals with the problem of robustly detecting and tracking humans and recognizing their attributes in challenging environments in real-time, from the egocentric perspective of a computationally constrained mobile robot equipped with multiple sensing modalities. The ability to perceive humans in their surroundings is a key ingredient for robots that operate in environments shared with humans, for example in consumer, industrial and automotive applications – such as a service robot for person guidance, an autonomous forklift in a warehouse, or a self-driving vehicle.
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Finally, the first promising experimental results achieved for people detection and tracking in a real-world environment (our institute building) are presented.
#SHOTBOT PARA GUNZ 2017 UPDATE#
The main advantages of this approach are the simple extensibility by the integration of further sensory channels, even with different update frequencies, and the usability in real-world HRI tasks. These probability distributions are further merged into a robot-centered map by means of a flexible probabilistic aggregation scheme based on Covariance Intersection (CI). For each of these sensory systems, separate and specific Gaussian probability distributions are generated to model the belief in observing one or several persons. These include a laser range-finder, a sonar system, and a fisheye-based omni-directional camera. In this paper, we introduce a new approach for the integration of several sensor modalities and present a multi-modal, probability-based people detection and tracking system and its application using the different sensory systems of our mobile interaction robot Horos. Efficient and robust techniques for people detection and tracking are basic prerequisites when dealing with Human–Robot Interaction (HRI) in real-world scenarios.