Hand gesture recognition is still a topic of great interest for the computer vision community. In particular, sign language and semaphoric hand gestures are two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. Any hand gesture can be represented by sets of feature vectors that change over time. Recurrent Neural Networks (RNNs) are suited to analyse this type of sets thanks to their ability to model the long term contextual information of temporal sequences. In this paper, a RNN is trained by using as features the angles formed by the finger bones of the human hands. The selected features, acquired by a Leap Motion Controller (LMC) sensor, are chosen because the majority of human hand gestures produce joint movements that generate truly characteristic corners. The proposed method, including the effectiveness of the selected angles, was initially tested by creating a very challenging dataset composed by a large number of gestures defined by the American Sign Language (ASL). On the latter, an accuracy of over 96% was achieved. Afterwards, by using the SHREC dataset, a wide collection of semaphoric hand gestures, the method was also proven to outperform in accuracy competing approaches of the current literature.

Deep learning approaches have recently raised the bar in many fields, from Natural Language Processing to Computer Vision, by leveraging large amounts of data. However, they could fail when the retrieved information is not enough to fit the vast number of parameters, frequently resulting in overfitting and therefore in poor generalizability. Few-Shot Learning aims at designing models that can effectively operate in a scarce data regime, yielding learning strategies that only need few supervised examples to be trained. These procedures are of both practical and theoretical importance, as they are crucial for many real-life scenarios in which data is either costly or even impossible to retrieve. Moreover, they bridge the distance between current data-hungry models and human-like generalization capability. Computer vision offers various tasks that can be few-shot inherent, such as person re-identification. This survey, which to the best of our knowledge is the first tackling this problem, is focused on Few-Shot Object Detection, which has received far less attention compared to Few-Shot Classification due to the intrinsic challenge level. In this regard, this review presents an extensive description of the approaches that have been tested in the current literature, discussing their pros and cons, and classifying them according to a rigorous taxonomy.

Tracking objects across multiple video frames is a challenging task due to several difficult issues such as occlusions, background clutter, lighting as well as object and camera view-point variations, which directly affect the object detection. These aspects are even more emphasized when analyzing unmanned aerial vehicles (UAV) based images, where the vehicle movement can also impact the image quality. A common strategy employed to address these issues is to analyze the input images at different scales to obtain as much information as possible to correctly detect and track the objects across video sequences. Following this rationale, in this paper, we introduce a simple yet effective novel multi-stream (MS) architecture, where different kernel sizes are applied to each stream to simulate a multi-scale image analysis. The proposed architecture is then used as backbone for the well-known Faster-R-CNN pipeline, defining a MS-Faster R-CNN object detector that consistently detects objects in video sequences. Subsequently, this detector is jointly used with the Simple Online and Real-time Tracking with a Deep Association Metric (Deep SORT) algorithm to achieve real-time tracking capabilities on UAV images. To assess the presented architecture, extensive experiments were performed on the UMCD, UAVDT, UAV20L, and UAV123 datasets. The presented pipeline achieved state-of-the-art performance, confirming that the proposed multi-stream method can correctly emulate the robust multi-scale image analysis paradigm.

Action recognition in video sequences is an interesting field for many computer vision applications, including behaviour analysis, event recognition, and video surveillance. In this work, a method based on 2D skeleton and two-branch stacked Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells is proposed. Unlike 3D skeletons, usually generated by RGB-D cameras, the 2D skeletons adopted in this work are reconstructed starting from RGB video streams, therefore allowing the use of the proposed approach in both indoor and outdoor environments. Moreover, any case of missing skeletal data is managed by exploiting 3D-Convolutional Neural Networks (3D-CNNs). Comparative experiments with several key works on KTH and Weizmann datasets show that the method described in this paper outperforms the current state-of-the-art. Additional experiments on UCF Sports and IXMAS datasets demonstrate the effectiveness of our method in the presence of noisy data and perspective changes, respectively. Further investigations on UCF Sports, HMDB51, UCF101, and Kinetics400 highlight how the combination between the proposed two-branch stacked LSTM and the 3D-CNN-based network can manage missing skeleton information, greatly improving the overall accuracy. Moreover, additional tests on KTH and UCF Sports datasets also show the robustness of our approach in the presence of partial body occlusions. Finally, comparisons on UT-Kinect and NTU-RGB+D datasets show that the accuracy of the proposed method is fully comparable to that of works based on 3D skeletons.

The internal inspection of large pipeline infrastructures, such as sewers and waterworks, is a fundamental task for the prevention of possible failures. In particular, visual inspection is typically performed by human operators on the basis of video sequences either acquired on-line or recorded for further off-line analysis. In this work, we propose a vision-based software approach to assist the human operator by conveniently showing the acquired data and by automatically detecting and highlighting the pipeline sections where relevant anomalies could occur.

We address the problem of acoustic source localization using a microphone array mounted on multirotor unmanned aerial vehicles (UAVs). Conventional localization beamforming techniques are especially challenging in these specific conditions, due to the nature and intensity of the disturbances affecting the recorded acoustic signals. The principal disturbances are related to the high frequency, narrowband noise originated by the electrical engines, and to the broadband aerodynamic noise induced by the propellers. A solution to this problem is proposed, which adopts an efficient beamforming technique for the direction of arrival (DOA) estimation of an acoustic source and a circular array detached from the multirotor vehicle body in order to reduce the effects of noise generated by the propellers. The approach used to localize the source relies on a diagonal unloading (DU) beamforming with a novel norm transform (NORT) frequency fusion. The proposed algorithm was tested on a multirotor UAV equipped with a compact uniform circular array (UCA) of eight microphones, placed on the bottom of the drone to localize the target acoustic source placed on the ground while the quadcopter is hovering at different altitudes. The experimental results conducted in outdoor hovering conditions are illustrated, and the localization performances are reported under various recording conditions and source characteristics.