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Dimitrios Bouzoulas

Publications and source records attributed to Dimitrios Bouzoulas.

2 recordsLinked to original sources
arXiv2026

Automatic Labelling for Low-Light Pedestrian Detection

Pedestrian detection in RGB images is a key task in pedestrian safety, as the most common sensor in autonomous vehicles and advanced driver assistance systems is the RGB camera. Low-light pedestrian detection lacks large public datasets and autolabelling pipelines. This research proposes a solution in the form of an automated infrared-RGB pipeline. The pipeline consists of 1) Infrared detection, where a fine-tuned model for infrared pedestrian detection is used 2) Label transfer process from the infrared detections to their RGB counterparts 3) Training object detection models using the generated labels for low-light RGB pedestrian detection. The research was performed using the KAIST dataset. For evaluation, three object detection models, DETR, YOLO, and RCNN, were trained on generated and ground truth labels. When compared on previously unseen images, the results showed that the models trained on generated labels out-performed the ones trained on ground-truth in 5 out of 6 cases for the mAP@50 and LAMR metrics, and outperformed ground-truth on mAP@50-95 in all cases. Acquired results indicate that the proposed auto-labelling pipeline could be used for scalable annotation of low-light datasets for pedestrian detection. The source code for this research is available on GitHub: https://github.com/BouzoulasDimitrios/IR-RGB-autoamed-low-light-pedestrian-labelling

cs.CV↗
arXiv2026

Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering

Event cameras provide high-temporal-resolution visual sensing that is well suited for observing fast-moving aerial objects; however, their use for drone trajectory prediction remains limited. This work introduces an event-only drone forecasting method that exploits propeller-induced motion cues. Propeller rotational speed are extracted directly from raw event data and fused within an RPM-aware Kalman filtering framework. Evaluations on the FRED dataset show that the proposed method outperforms learning-based approaches and vanilla kalman filter in terms of average distance error and final distance error at 0.4s and 0.8s forecasting horizons. The results demonstrate robust and accurate short- and medium-horizon trajectory forecasting without reliance on RGB imagery or training data.

cs.CV↗