Mojtaba Hosseini

Grzegorz Ficht, Hafez Farazi, André Brandenburger et al.

Humanoid robotics research depends on capable robot platforms, but recently developed advanced platforms are often not available to other research groups, expensive, dangerous to operate, or closed-source. The lack of available platforms forces researchers to work with smaller robots, which have less strict dynamic constraints or with simulations, which lack many real-world effects. We developed NimbRo-OP2X to address this need. At a height of 135 cm our robot is large enough to interact in a human environment. Its low weight of only 19 kg makes the operation of the robot safe and easy, as no special operational equipment is necessary. Our robot is equipped with a fast onboard computer and a GPU to accelerate parallel computations. We extend our already open-source software by a deep-learning based vision system and gait parameter optimisation. The NimbRo-OP2X was evaluated during RoboCup 2018 in Montréal, Canada, where it won all possible awards in the Humanoid AdultSize class.

Hafez Farazi, Grzegorz Ficht, Philipp Allgeuer et al.

Over the past few years, the Humanoid League rules have changed towards more realistic and challenging game environments, which encourage teams to advance their robot soccer performances. In this paper, we present the software and hardware designs that led our team NimbRo to win the competitions in the AdultSize league -- including the soccer tournament, the drop-in games, and the technical challenges at RoboCup 2018 in Montreal. Altogether, this resulted in NimbRo winning the Best Humanoid Award. In particular, we describe our deep-learning approaches for visual perception and our new fully 3D printed robot NimbRo-OP2X.

Mohammad Sabokrou, Mahmood Fathy, Mojtaba Hosseini et al.

In this paper, we propose a method for real-time anomaly detection and localization in crowded scenes. Each video is defined as a set of non-overlapping cubic patches, and is described using two local and global descriptors. These descriptors capture the video properties from different aspects. By incorporating simple and cost-effective Gaussian classifiers, we can distinguish normal activities and anomalies in videos. The local and global features are based on structure similarity between adjacent patches and the features learned in an unsupervised way, using a sparse auto- encoder. Experimental results show that our algorithm is comparable to a state-of-the-art procedure on UCSD ped2 and UMN benchmarks, but even more time-efficient. The experiments confirm that our system can reliably detect and localize anomalies as soon as they happen in a video.

Mohammad Sabokrou, Mahmood Fathy, Mojtaba Hosseini

In this paper, we propose an accurate and real-time anomaly detection and localization in crowded scenes, and two descriptors for representing anomalous behavior in video are proposed. We consider a video as being a set of cubic patches. Based on the low likelihood of an anomaly occurrence, and the redundancy of structures in normal patches in videos, two (global and local) views are considered for modeling the video. Our algorithm has two components, for (1) representing the patches using local and global descriptors, and for (2) modeling the training patches using a new representation. We have two Gaussian models for all training patches respect to global and local descriptors. The local and global features are based on structure similarity between adjacent patches and the features that are learned in an unsupervised way. We propose a fusion strategy to combine the two descriptors as the output of our system. Experimental results show that our algorithm performs like a state-of-the-art method on several standard datasets, but even is more time-efficient.