M4Depth: A motion-based approach for monocular depth estimation on video sequences

Getting the distance to objects is crucial for autonomous vehicles. In instances where depth sensors cannot be used, this distance has to be estimated from RGB cameras. As opposed to cars, the task of estimating depth from on-board mounted cameras is made complex on drones because of the lack of constrains on motion during flights. In this paper, we present a method to estimate the distance of objects seen by an on-board mounted camera by using its RGB video stream and drone motion information. Our method is built upon a pyramidal convolutional neural network architecture and uses time recurrence in pair with geometric constraints imposed by motion to produce pixel-wise depth maps. In our architecture, each level of the pyramid is designed to produce its own depth estimate based on past observations and information provided by the previous level in the pyramid. We introduce a spatial reprojection layer to maintain the spatio-temporal consistency of the data between the levels. We analyse the performance of our approach on Mid-Air, a public drone dataset featuring synthetic drone trajectories recorded in a wide variety of unstructured outdoor environments. Our experiments show that our network outperforms state-of-the-art depth estimation methods and that the use of motion information is the main contributing factor for this improvement. The code of our method is publicly available on GitHub; see https://github.com/michael-fonder/M4Depth

Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting

Soccer broadcast video understanding has been drawing a lot of attention in recent years within data scientists and industrial companies. This is mainly due to the lucrative potential unlocked by effective deep learning techniques developed in the field of computer vision. In this work, we focus on the topic of camera calibration and on its current limitations for the scientific community. More precisely, we tackle the absence of a large-scale calibration dataset and of a public calibration network trained on such a dataset. Specifically, we distill a powerful commercial calibration tool in a recent neural network architecture on the large-scale SoccerNet dataset, composed of untrimmed broadcast videos of 500 soccer games. We further release our distilled network, and leverage it to provide 3 ways of representing the calibration results along with player localization. Finally, we exploit those representations within the current best architecture for the action spotting task of SoccerNet-v2, and achieve new state-of-the-art performances.

SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos

Understanding broadcast videos is a challenging task in computer vision, as it requires generic reasoning capabilities to appreciate the content offered by the video editing. In this work, we propose SoccerNet-v2, a novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production. Specifically, we release around 300k annotations within SoccerNet's 500 untrimmed broadcast soccer videos. We extend current tasks in the realm of soccer to include action spotting, camera shot segmentation with boundary detection, and we define a novel replay grounding task. For each task, we provide and discuss benchmark results, reproducible with our open-source adapted implementations of the most relevant works in the field. SoccerNet-v2 is presented to the broader research community to help push computer vision closer to automatic solutions for more general video understanding and production purposes.

Multimodal and multiview distillation for real-time player detection on a football field

Monitoring the occupancy of public sports facilities is essential to assess their use and to motivate their construction in new places. In the case of a football field, the area to cover is large, thus several regular cameras should be used, which makes the setup expensive and complex. As an alternative, we developed a system that detects players from a unique cheap and wide-angle fisheye camera assisted by a single narrow-angle thermal camera. In this work, we train a network in a knowledge distillation approach in which the student and the teacher have different modalities and a different view of the same scene. In particular, we design a custom data augmentation combined with a motion detection algorithm to handle the training in the region of the fisheye camera not covered by the thermal one. We show that our solution is effective in detecting players on the whole field filmed by the fisheye camera. We evaluate it quantitatively and qualitatively in the case of an online distillation, where the student detects players in real time while being continuously adapted to the latest video conditions.

Summarizing the performances of a background subtraction algorithm measured on several videos

There exist many background subtraction algorithms to detect motion in videos. To help comparing them, datasets with ground-truth data such as CDNET or LASIESTA have been proposed. These datasets organize videos in categories that represent typical challenges for background subtraction. The evaluation procedure promoted by their authors consists in measuring performance indicators for each video separately and to average them hierarchically, within a category first, then between categories, a procedure which we name "summarization". While the summarization by averaging performance indicators is a valuable effort to standardize the evaluation procedure, it has no theoretical justification and it breaks the intrinsic relationships between summarized indicators. This leads to interpretation inconsistencies. In this paper, we present a theoretical approach to summarize the performances for multiple videos that preserves the relationships between performance indicators. In addition, we give formulas and an algorithm to calculate summarized performances. Finally, we showcase our observations on CDNET 2014.

Real-Time Semantic Background Subtraction

Semantic background subtraction SBS has been shown to improve the performance of most background subtraction algorithms by combining them with semantic information, derived from a semantic segmentation network. However, SBS requires high-quality semantic segmentation masks for all frames, which are slow to compute. In addition, most state-of-the-art background subtraction algorithms are not real-time, which makes them unsuitable for real-world applications. In this paper, we present a novel background subtraction algorithm called Real-Time Semantic Background Subtraction (denoted RT-SBS) which extends SBS for real-time constrained applications while keeping similar performances. RT-SBS effectively combines a real-time background subtraction algorithm with high-quality semantic information which can be provided at a slower pace, independently for each pixel. We show that RT-SBS coupled with ViBe sets a new state of the art for real-time background subtraction algorithms and even competes with the non real-time state-of-the-art ones. Note that python CPU and GPU implementations of RT-SBS will be released soon.

A Context-Aware Loss Function for Action Spotting in Soccer Videos

Action spotting is an important element of general activity understanding. It consists of detecting human-induced events annotated with single timestamps. In this paper, we propose a novel loss function for action spotting. Our loss aims at dealing specifically with the temporal context naturally present around an action. Rather than focusing on the single annotated frame of the action to spot, we consider different temporal segments surrounding it and shape our loss function accordingly. We test our loss on SoccerNet, a large dataset of soccer videos, showing an improvement of 12.8% on the current baseline. We also show the generalization capability of our loss function on ActivityNet for activity proposals and detection, by spotting the beginning and the end of each activity. Furthermore, we provide an extended ablation study and identify challenging cases for action spotting in soccer videos. Finally, we qualitatively illustrate how our loss induces a precise temporal understanding of actions, and how such semantic knowledge can be leveraged to design a highlights generator.

HitNet: a neural network with capsules embedded in a Hit-or-Miss layer, extended with hybrid data augmentation and ghost capsules

Neural networks designed for the task of classification have become a commodity in recent years. Many works target the development of better networks, which results in a complexification of their architectures with more layers, multiple sub-networks, or even the combination of multiple classifiers. In this paper, we show how to redesign a simple network to reach excellent performances, which are better than the results reproduced with CapsNet on several datasets, by replacing a layer with a Hit-or-Miss layer. This layer contains activated vectors, called capsules, that we train to hit or miss a central capsule by tailoring a specific centripetal loss function. We also show how our network, named HitNet, is capable of synthesizing a representative sample of the images of a given class by including a reconstruction network. This possibility allows to develop a data augmentation step combining information from the data space and the feature space, resulting in a hybrid data augmentation process. In addition, we introduce the possibility for HitNet, to adopt an alternative to the true target when needed by using the new concept of ghost capsules, which is used here to detect potentially mislabeled images in the training data.