Video-based trajectory analysis might be rather well discussed in sports, such as soccer or basketball, but in cycling, this is far less common. In this paper, a video processing pipeline to extract riding lines in cyclocross races is presented. The pipeline consists of a stepwise analysis process to extract riding behavior from a region (i.e., the fence) in a video camera feed. In the first step, the riders are identified by an Alphapose skeleton detector and tracked with a spatiotemporally aware pose tracker. Next, each detected pose is enriched with additional meta-information, such as rider modus (e.g., sitting on the saddle or standing on the pedals) and detected team (based on the worn jerseys). Finally, a post-processor brings all the information together and proposes ride lines with meta-information for the riders in the fence. The presented methodology can provide interesting insights, such as intra-athlete ride line clustering, anomaly detection, and detailed breakdowns of riding and running durations within the segment. Such detailed rider info can be very valuable for performance analysis, storytelling, and automatic summarization.

Personally curated content in short-form video formats provides added value for participants and spectators but is often disregarded in lower-level events because it is too labor-intensive to create or is not recorded at all. Our smart sensor-driven tripod focuses on supplying a unified sensor and video solution to capture personalized highlights for participants in various sporting events with low computational and hardware costs. The relevant parts of the video for each participant are automatically determined by using the timestamps of his/her received sensor data. This is achieved through a customizable clipping mechanism that processes and optimizes both video and sensor data. The clipping mechanism is driven by sensing nearby signals of Adaptive Network Topology (ANT+) capable devices worn by the athletes that provide both locality information and identification. The device was deployed and tested in an amateur-level cycling race in which it provided clips with a detection rate of 92.9%. The associated sensor data were used to automatically extract peloton passages and report riders’ positions on the course, as well as which participants were grouped together. Insights derived from sensor signals can be processed and published in real time, and an upload optimization scheme is proposed that can provide video clips for each rider a maximum of 5 min after the passage if video upload is enabled.

SmarterRoutes aims to improve navigational services and make them more dynamic and personalised by data-driven and environmentally-aware road scene complexity estimation. SmarterRoutes divides complexity into two subtypes: perceived and descriptive complexity. In the SmarterRoutes architecture, the overall road scene complexity is indicated by combining and merging parameters from both types of complexity. Descriptive complexity is derived from geospatial data sources, traffic data and sensor analysis. The architecture is currently using OpenStreetMap (OSM) tag analysis, Meten-In-Vlaanderen (MIV) derived traffic info and the Alaro weather model of the Royal Meteorological Institute of Belgium (RMI) as descriptive complexity indicators. For the perceived complexity an image based complexity estimation mechanism is presented. This image based Densenet Convolutional Neural Network (CNN) uses Street View images as input and was pretrained on buildings with Bag-of-Words and Structure-from-motion features. The model calculates an image descriptor allowing comparison of images by calculation of the Euclidean distances between descriptors. SmarterRoutes extends this model by additional hand-labelled rankings of road scene images to predict visual road complexity. The reuse of an existing pretrained model with an additional ranking mechanism produces results corresponding with subjective assessments of end-users. Finally, the global complexity mechanism combines the aforementioned sub-mechanisms and produces a service which should facilitate user-centred context-aware navigation by intelligent data selection and/or omission based on SmarterRoutes’ complexity input.

Video-based analysis of cycling races can provide a lot of information that can be used to keep cycling interesting for the fans and improve cyclists' safety. In this paper, we propose a solution to collect and process the metadata of cycling races. The idea is to use edge computing, by collecting data from a car in front of the race and processing this data using a tailor-made setup. Our solution consists of a camera to record video, and a GPS module to map the corresponding locations. Both data streams are offered to a single board computer. The video frames are used for crowd size classification to roughly estimate the number of spectators present along the race route. Moreover, we use the same footage to recognize cyclists' names on the road's surface to determine the location of fans of specific cyclists to create metadata around fan engagement. The tailor-made system performs the processing of the video frames and the results are sent to a web server using a cellular network connection. A web application was created to visualize the crowd size and the location of cyclists' names on the road's surface.