Advanced computer vision and machine learning technologies transform how we experience sports events. This work enriches helicopter footage of cycling races with dynamic, in-scene, pose-aligned augmented reality (AR) overlays (e.g., rider name, speed, wind direction) that remain visually attached to each rider. To achieve this, we propose a multi-stage pipeline: cyclists are first detected and tracked, followed by team recognition using a one-shot learning approach based on Siamese neural networks, which achieves a classification accuracy of 85% on a test set composed of unseen teams during training. This design allows easy adaptation and reuse across different races and seasons, enabling frequent jersey and team changes with minimal effort. We introduce a pose-based AR overlay that anchors rider labels to moving cyclists without fixed field landmarks or homography, enabling dynamic overlays in unconstrained cycling broadcasts. Real-time feasibility is demonstrated through runtime profiling and TensorRT optimizations. Finally, a user study evaluates the readability, informativeness, visual stability, and engagement of our AR-enhanced broadcasts. The combination of advanced computer vision, AR, and user-centered evaluation showcases new possibilities for improving live sports broadcasts, particularly in challenging environments like road cycling.

This paper proposes a generic methodology for semi-automatic generation of reliable position annotations for evaluating multi-camera people-trackers on large video datasets. Most of the annotation data is computed automatically, by estimating a consensus tracking result from multiple existing trackers and people detectors and classifying it as either reliable or not. A small subset of the data, composed of tracks with insufficient reliability is verified by a human using a simple binary decision task, a process faster than marking the correct person position. The proposed framework is generic and can handle additional trackers. We present results on a dataset of approximately 6 hours captured by 4 cameras, featuring a person in a holiday flat, performing activities such as walking, cooking, eating, cleaning, and watching TV. When aiming for a tracking accuracy of 60cm, 80% of all video frames are automatically annotated. The annotations for the remaining 20% of the frames were added after human verification of an automatically selected subset of data. This involved about 2.4 hours of manual labour. According to a subsequent comprehensive visual inspection to judge the annotation procedure, we found 99% of the automatically annotated frames to be correct. We provide guidelines on how to apply the proposed methodology to new datasets. We also provide an exploratory study for the multi-target case, applied on existing and new benchmark video sequences.

The trend towards mass customization has led to a significant increase of the complexity of manufacturing systems. Models to evaluate the complexity have been developed, but the complexity analysis of work stations is still done manually. This paper describes an automated analysis tool that makes us of multicamera video images to support the complexity analysis of assembly line work stations

Despite adding emotions to applications has proven to enhance the user experience, emotion recognition applications are still not widely available nor used. Within this paper, emotion recognition is done on Twitter tweets using six emotion classification algorithms that are compared on precision and timing. The paper shows that precision can be enhanced by 5.02% compared to the current state-of-the-art by improving the features. Furthermore, the presented algorithms work in real-time.