Coluccia, A, Fascista, A, Schumann, A, Sommer, L, Ghenescu, M, Piatrik, T, De Cubber, G, Nalamati, M, Kapoor, A, Saqib, M, Sharma, N, Blumenstein, M, Magoulianitis, V, Ataloglou, D, Dimou, A, Zarpalas, D, Daras, P, Craye, C, Ardjoune, S, De La Iglesia, D, Mendez, M, Dosil, R & Gonzalez, I 2019, 'Drone-vs-bird detection challenge at IEEE AVSS2019', 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019, IEEE International Conference on Advanced Video and Signal Based Surveillance, IEEE, Taipei, Taiwan.View/Download from: Publisher's site
© 2019 IEEE. This paper presents the second edition of the 'drone-vs-bird' detection challenge, launched within the activities of the 16-th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS). The challenge's goal is to detect one or more drones appearing at some point in video sequences where birds may be also present, together with motion in background or foreground. Submitted algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. This paper reports on the challenge results on the 2019 dataset, which extends the first edition dataset provided by the SafeShore project with additional footage under different conditions.
Nalamati, M, Kapoor, A, Saqib, M, Sharma, N & Blumenstein, M 2019, 'Drone detection in long-range surveillance videos', 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019, International Conference on Advanced Video and Signal Based Surveillance, IEEE, Taipei, Taiwan.View/Download from: UTS OPUS or Publisher's site
© 2019 IEEE. The usage of small drones/UAVs has significantly increased recently. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. The similarity in the appearance of small drone and birds in complex background makes it challenging to detect drones in surveillance videos. This paper addresses the challenge of detecting small drones in surveillance videos using popular and advanced deep learning-based object detection methods. Different CNN-based architectures such as ResNet-101 and Inception with Faster-RCNN, as well as Single Shot Detector (SSD) model was used for experiments. Due to sparse data available for experiments, pre-trained models were used while training the CNNs using transfer learning. Best results were obtained from experiments using Faster-RCNN with the base architecture of ResNet-101. Experimental analysis on different CNN architectures is presented in the paper, along with the visual analysis of the test dataset.