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The recent advancements in Deep learning has lead to new interesting applications such as analyzing human motion and activities in recorded videos. The analysis covers from simple motion of humans walking, performing exercises to complex motions such as playing sports.
The athlete’s performance can be easily captured with a fixed camera for sports like tennis, badminton, diving, etc. The large availability of low cost cameras in handheld devices has further led to common place solution to record videos and analyze an athletes performance. Although the sports trainers can provide visual feedback by playing recorded videos, it is still hard to measure and monitor the performance improvement of the athlete. Also, the manual analysis of the obtained footage is a time-consuming task which involves isolating actions of interest and categorizing them using domain-specific knowledge. Thus, the automatic interpretation of performance parameters in sports has gained a keen interest.
Competitive diving is one of the well recognized aquatic sport in Olympics in which a person dives from a platform or a springboard and performs different classes of acrobatics before descending into the water. These classes are standardized by international organization Fédération Internationale de Natation (FINA). The differences in the acrobatics performed in various classes of diving are very subtle. The difference arises in the duration which starts with the diver standing on a diving platform or a springboard and ends at the moment he/she dives into the water. This is a challenging task to model especially due to involvement of rapid changes and requires understanding of long-term human dynamics. Further, the model must be sensitive to subtle changes in body pose over a large number of frames to determine the correct classification.
In order to automate this kind of task, three challenging sub-problems are often encountered: 1) temporally cropping events/actions of interest from continuous video; 2) tracking the person of interest even though other divers and bystanders may be in view; and 3) classifying the events/actions of interest.
We are developing a solution in co-operation with Institut für Angewandte Trainingswissenshaft in Leipzig (IAT) to tackle the three subproblems. We work towards a complete parameter tracking solution based on monocular markerless human body motion tracking using only a mobile device (tablet or mobile phone) as training support tool to the overall diving action analysis. The techniques proposed, can be generalized to video footage recorded from other sports.
We are happy to announce that three papers with respect to our structured light 3D reconstruction pipeline have been accepted for publication at the CAIP 2021. The International Conference on Computer Analysis of Images and Patterns will take place from September 28th to 30th, 2021 as a virtual conference.
The three accepted papers are entitled ”Fast Projector-Driven Structured Light Matching in Sub-Pixel Accuracy using Bilinear Interpolation Assumption”, ”Simultaneous Bi-Directional Structured Light Encoding for Practical Uncalibrated Profilometry” and ”Joint Global ICP for Improved Automatic Alignment of Full Turn Object Scans” and will be available right after the conference.
DFKI participates in the VIZTA project, coordinated by ST Micrelectronics, aiming at developing innovative technologies in the field of optical sensors and laser sources for short to long-range 3D-imaging and to demonstrate their value in several key applications including automotive, security, smart buildings, mobile robotics for smart cities, and industry 4.0. The 24-month review by the EU-commission was completed and a public summary of the project was released, including updates from DFKI Augmented Vision on time-of-flight camera dataset recording and deep learning algorithm development for car in-cabin monitoring and smart building person counting and anomaly detection applications.
Please click here to check out the complete summary: https://www.vizta-ecsel.eu/newsletter-april-2021/