846 research outputs found
Indirect Match Highlights Detection with Deep Convolutional Neural Networks
Highlights in a sport video are usually referred as actions that stimulate
excitement or attract attention of the audience. A big effort is spent in
designing techniques which find automatically highlights, in order to
automatize the otherwise manual editing process. Most of the state-of-the-art
approaches try to solve the problem by training a classifier using the
information extracted on the tv-like framing of players playing on the game
pitch, learning to detect game actions which are labeled by human observers
according to their perception of highlight. Obviously, this is a long and
expensive work. In this paper, we reverse the paradigm: instead of looking at
the gameplay, inferring what could be exciting for the audience, we directly
analyze the audience behavior, which we assume is triggered by events happening
during the game. We apply deep 3D Convolutional Neural Network (3D-CNN) to
extract visual features from cropped video recordings of the supporters that
are attending the event. Outputs of the crops belonging to the same frame are
then accumulated to produce a value indicating the Highlight Likelihood (HL)
which is then used to discriminate between positive (i.e. when a highlight
occurs) and negative samples (i.e. standard play or time-outs). Experimental
results on a public dataset of ice-hockey matches demonstrate the effectiveness
of our method and promote further research in this new exciting direction.Comment: "Social Signal Processing and Beyond" workshop, in conjunction with
ICIAP 201
F-formation Detection: Individuating Free-standing Conversational Groups in Images
Detection of groups of interacting people is a very interesting and useful
task in many modern technologies, with application fields spanning from
video-surveillance to social robotics. In this paper we first furnish a
rigorous definition of group considering the background of the social sciences:
this allows us to specify many kinds of group, so far neglected in the Computer
Vision literature. On top of this taxonomy, we present a detailed state of the
art on the group detection algorithms. Then, as a main contribution, we present
a brand new method for the automatic detection of groups in still images, which
is based on a graph-cuts framework for clustering individuals; in particular we
are able to codify in a computational sense the sociological definition of
F-formation, that is very useful to encode a group having only proxemic
information: position and orientation of people. We call the proposed method
Graph-Cuts for F-formation (GCFF). We show how GCFF definitely outperforms all
the state of the art methods in terms of different accuracy measures (some of
them are brand new), demonstrating also a strong robustness to noise and
versatility in recognizing groups of various cardinality.Comment: 32 pages, submitted to PLOS On
The Visual Social Distancing Problem
One of the main and most effective measures to contain the recent viral
outbreak is the maintenance of the so-called Social Distancing (SD). To comply
with this constraint, workplaces, public institutions, transports and schools
will likely adopt restrictions over the minimum inter-personal distance between
people. Given this actual scenario, it is crucial to massively measure the
compliance to such physical constraint in our life, in order to figure out the
reasons of the possible breaks of such distance limitations, and understand if
this implies a possible threat given the scene context. All of this, complying
with privacy policies and making the measurement acceptable. To this end, we
introduce the Visual Social Distancing (VSD) problem, defined as the automatic
estimation of the inter-personal distance from an image, and the
characterization of the related people aggregations. VSD is pivotal for a
non-invasive analysis to whether people comply with the SD restriction, and to
provide statistics about the level of safety of specific areas whenever this
constraint is violated. We then discuss how VSD relates with previous
literature in Social Signal Processing and indicate which existing Computer
Vision methods can be used to manage such problem. We conclude with future
challenges related to the effectiveness of VSD systems, ethical implications
and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this
manuscript and they are listed by alphabetical order. Under submissio
MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses
Recent approaches on trajectory forecasting use tracklets to predict the
future positions of pedestrians exploiting Long Short Term Memory (LSTM)
architectures. This paper shows that adding vislets, that is, short sequences
of head pose estimations, allows to increase significantly the trajectory
forecasting performance. We then propose to use vislets in a novel framework
called MX-LSTM, capturing the interplay between tracklets and vislets thanks to
a joint unconstrained optimization of full covariance matrices during the LSTM
backpropagation. At the same time, MX-LSTM predicts the future head poses,
increasing the standard capabilities of the long-term trajectory forecasting
approaches. With standard head pose estimators and an attentional-based social
pooling, MX-LSTM scores the new trajectory forecasting state-of-the-art in all
the considered datasets (Zara01, Zara02, UCY, and TownCentre) with a dramatic
margin when the pedestrians slow down, a case where most of the forecasting
approaches struggle to provide an accurate solution.Comment: 10 pages, 3 figures to appear in CVPR 201
Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets
In this work, we explore the correlation between people trajectories and
their head orientations. We argue that people trajectory and head pose
forecasting can be modelled as a joint problem. Recent approaches on trajectory
forecasting leverage short-term trajectories (aka tracklets) of pedestrians to
predict their future paths. In addition, sociological cues, such as expected
destination or pedestrian interaction, are often combined with tracklets. In
this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between
positions and head orientations (vislets) thanks to a joint unconstrained
optimization of full covariance matrices during the LSTM backpropagation. We
additionally exploit the head orientations as a proxy for the visual attention,
when modeling social interactions. MX-LSTM predicts future pedestrians location
and head pose, increasing the standard capabilities of the current approaches
on long-term trajectory forecasting. Compared to the state-of-the-art, our
approach shows better performances on an extensive set of public benchmarks.
MX-LSTM is particularly effective when people move slowly, i.e. the most
challenging scenario for all other models. The proposed approach also allows
for accurate predictions on a longer time horizon.Comment: Accepted at IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE
INTELLIGENCE 2019. arXiv admin note: text overlap with arXiv:1805.0065
Quantum Gravity Fluctuations in the Timelike Raychaudhuri Equation
We consider a timelike geodesic congruence in the presence of perturbative
quantum fluctuations of the spacetime metric. We calculate the change in the
volume of a bundle of geodesics due to such fluctuations and thereby obtain a
quantum-gravitationally modified timelike Raychaudhuri equation. Quantum
gravity generically increases the convergence of congruences and the production
of caustics.Comment: 12 pages, 2 figure
A deep Natural Language Inference predictor without language-specific training data
In this paper we present a technique of NLP to tackle the problem of
inference relation (NLI) between pairs of sentences in a target language of
choice without a language-specific training dataset. We exploit a generic
translation dataset, manually translated, along with two instances of the same
pre-trained model - the first to generate sentence embeddings for the source
language, and the second fine-tuned over the target language to mimic the
first. This technique is known as Knowledge Distillation. The model has been
evaluated over machine translated Stanford NLI test dataset, machine translated
Multi-Genre NLI test dataset, and manually translated RTE3-ITA test dataset. We
also test the proposed architecture over different tasks to empirically
demonstrate the generality of the NLI task. The model has been evaluated over
the native Italian ABSITA dataset, on the tasks of Sentiment Analysis,
Aspect-Based Sentiment Analysis, and Topic Recognition. We emphasise the
generality and exploitability of the Knowledge Distillation technique that
outperforms other methodologies based on machine translation, even though the
former was not directly trained on the data it was tested over.Comment: Conference: ICIAP202
POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments
In this paper we focus on the problem of learning an optimal policy for Active Visual Search (AVS) of objects in known indoor environments with an online setup. Our POMP method uses as input the current pose of an agent (e.g. a robot) and a RGB-D frame. The task is to plan the next move that brings the agent closer to the target object. We model this problem as a Partially Observable Markov Decision Process solved by a Monte-Carlo planning approach. This allows us to make decisions on the next moves by iterating over the known scenario at hand, exploring the environment and searching for the object at the same time. Differently from the current state of the art in Reinforcement Learning, POMP does not require extensive and expensive (in time and computation) labelled data so being very agile in solving AVS in small and medium real scenarios. We only require the information of the floormap of the environment, an information usually available or that can be easily extracted from an a priori single exploration run. We validate our method on the publicly available AVD benchmark, achieving an average success rate of 0.76 with an average path length of 17.1, performing close to the state of the art but without any training needed. Additionally, we show experimentally the robustness of our method when the quality of the object detection goes from ideal to faulty
POMP++: Pomcp-based Active Visual Search in unknown indoor environments
In this paper we focus on the problem of learning online an optimal policy for Active Visual Search (AVS) of objects in unknown indoor environments. We propose POMP++, a planning strategy that introduces a novel formulation on top of the classic Partially Observable Monte Carlo Planning (POMCP) framework, to allow training-free online policy learning in unknown environments. We present a new belief reinvigoration strategy which allows to use POMCP with a dynamically growing state space to address the online generation of the floor map. We evaluate our method on two public benchmark datasets, AVD that is acquired by real robotic platforms and Habitat ObjectNav that is rendered from real 3D scene scans, achieving the best success rate with an improvement of >10% over the state-of-the-art methods
Recognition self-awareness for active object recognition on depth images
We propose an active object recognition framework that introduces the recognition self-awareness, which is an intermediate level of reasoning to decide which views to cover during the object exploration. This is built first by learning a multi-view deep 3D object classifier; subsequently, a 3D dense saliency volume is generated by fusing together single-view visualization maps, these latter obtained by computing the gradient map of the class label on different image planes. The saliency volume indicates which object parts the classifier considers more important for deciding a class. Finally, the volume is injected in the observation model of a Partially Observable Markov Decision Process (POMDP). In practice, the robot decides which views to cover, depending on the expected ability of the classifier to discriminate an object class by observing a specific part. For example, the robot will look for the engine to discriminate between a bicycle and a motorbike, since the classifier has found that part as highly discriminative. Experiments are carried out on depth images with both simulated and real data, showing that our framework predicts the object class with higher accuracy and lower energy consumption than a set of alternatives
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