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Datasets

TUB CrowdFlow Dataset

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© Fachgebiet Nachrichtenübertragung

Optical Flow Dataset and Benchmark for Visual Crowd Analysis. A new optical flow dataset exploiting the possibilities of a recent video engine to generate sequences with groundtruth optical flow for large crowds in different scenarios. We break with the development of the last decade of introducing ever increasing displacements to pose new difficulties. Instead we focus on real-world surveillance scenarios where numerous small, partly independent, non rigidly moving objects observed over a long temporal range pose a challenge. more to: TUB CrowdFlow Dataset

Multi-Object and Multi-Camera Tracking Dataset (MOCAT)

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© Fachgebiet Nachrichtenübertragung

The TU Berlin Multi-Object and Multi-Camera Tracking Dataset (MOCAT) is a synthetic dataset to train and test tracking and detection systems in a virtual world. One of the key advantages of this dataset is that there is a complete and accurate ground truth, including pixel accurate object masks, available. All sequences are rendered 3 times, each with different illumination settings. This allows to directly measure the influence of the illumination to the algorithm under test. There are 8 to 10 different camera views (including camera calibration information) with partly overlapping FOVs for each sequence available. The ground truth contains the world position for each object, so the multi-camera tracking performance can be evaluated as well. All sequences contain vehicles, animals and pedestrians as objects to detect and track. more to: Multi-Object and Multi-Camera Tracking Dataset (MOCAT)

Software

IOU Tracker @ GITHUB

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© Copyright??

Tracking-by-detection is a common approach to multi-object tracking. With ever increasing performances of object detectors, the basis for a tracker becomes much more reliable. In combination with commonly higher frame rates, this poses a shift in the challenges for a successful tracker. We propose a very simple tracking algorithm which can compete with more sophisticated approaches at a fraction of the computational cost. This GIT provides the Python implementation of the IOU Tracker. more to: IOU Tracker @ GITHUB

Robust Local Optical Flow Library (RLOF) @ OpenCV contrib (4.1)

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© Communications Systems Group (TU-Berlin)

The Robust Local Optical Flow (RLOF) is a sparse optical flow and feature tracking method. We are deligthed that it is now part of OpenCV Contribution library (4.1.0). The RLOF methods are motivated by the problem of local motion estimation via robust regression with linear models. The main objective is to provide real-time capability, accurate and scaleable motion estimation solution. The software implements several versions of the RLOF algorithms for sparse and dense optical flow estimation. more to: Robust Local Optical Flow Library (RLOF) @ OpenCV contrib (4.1)

Background Substraction Library (SGMM-SOD)

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© Communications Systems Group (TU-Berlin)

We provide binaries of the SGMM-SOD library in order to help other researchers to compare their results or to use our work as a module for their research. The files contain a binary package for the Windows operating system and a minimal example on how to use the library. We have tried to keep the interface as simple as possible more to: Background Substraction Library (SGMM-SOD)

Evaluation framework for abandoned object detection

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© Communications Systems Group (TU-Berlin)

Since the commonly used benchmarks for abandoned object detection (AOD) only have few abandoned objects and a non-standardized evaluation procedure, an objective performance comparison between different methods is hard. Therefore, we propose a new evaluation metric which is focused on an end-user application case and an evaluation protocol which eliminates uncertainties in previous performance assessments. more to: Evaluation framework for abandoned object detection

Regularized Gradient Descent Training of Steered Mixture of Experts for Sparse Image Representation (tf-smoe)

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© Communications Systems Group (TU-Berlin)

A Tensorflow-based implementation of the Steered Mixture-of-Experts (SMoE) image modelling approach described in the ICIP 2018 paper Regularized Gradient Descent Training of Steered Mixture of Experts for Sparse Image Representation. This repository contains the implementation of a reference class for training and regressing a SMoE model, an easy to use training script as well as a jupyter notebook which helps you getting started writing you own code with a more elaborate example. more to: Regularized Gradient Descent Training of Steered Mixture of Experts for Sparse Image Representation (tf-smoe)

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