Video Content Analysis

Group Description

The interest in automated video analysis solutions has constantly increased throughout the past years. Video analysis methods applied under mainly controlled conditions, such as industrial environments, are nowadays an established technology. Despite great progress this field, the application auf video analysis techniques under uncontrolled conditions is still a widely unsolved problem. Major challenges mainly stem from the complexity and variability of unstructured outdoor environments. Additional challenges are e.g. objects showing a strong shape variation. Taking these challenges into account, we mainly focus on the following research questions:

  • Robustness. The focus lies on the analysis of video data originating from multi-spectral sensor platforms, which can freely move through unstructured environments. This includes the detection and tracking of objects, as well as the development of efficient preprocessing mechanisms capable of supporting automated scene understanding.
  • Adaptivity. Video analysis systems, applied in unknown environments must be able to adapt themselves automatically to specific conditions up to a certain degree.  Adaptation can be done “offline” by applying statistical approaches, as well as through methods that automatically learn from the environment.
  • Scalability. Current video analytic is mainly restricted to a limited number of object categories or events which are robustly detectable. In general there is a strong interest in methods showing advanced discriminative capabilities. Towards this end the question on how to scale up video analysis systems is of significant importance. 

Datenset Single Trajectory Sanity Check Benchmark

The datasets provides a benchmark for machine learning path prediction tasks. The benchmark and further details are available GitHub. Download.

Dataset Multispectral Action Dataset

The Multispectral Action Dataset containts video sequences showing violent and non-violent behaviour, recorded in the visible and the infrared spectrum. The dataset is freely available on request.

Recent thesis

T. Kostov, „Integration of Observation Uncertainty into an RNN-based Prediction Network“, Masterarbeit, KIT, 2019

Recent publications

Selected publications