10,750 research outputs found
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure
Realtime Multilevel Crowd Tracking using Reciprocal Velocity Obstacles
We present a novel, realtime algorithm to compute the trajectory of each
pedestrian in moderately dense crowd scenes. Our formulation is based on an
adaptive particle filtering scheme that uses a multi-agent motion model based
on velocity-obstacles, and takes into account local interactions as well as
physical and personal constraints of each pedestrian. Our method dynamically
changes the number of particles allocated to each pedestrian based on different
confidence metrics. Additionally, we use a new high-definition crowd video
dataset, which is used to evaluate the performance of different pedestrian
tracking algorithms. This dataset consists of videos of indoor and outdoor
scenes, recorded at different locations with 30-80 pedestrians. We highlight
the performance benefits of our algorithm over prior techniques using this
dataset. In practice, our algorithm can compute trajectories of tens of
pedestrians on a multi-core desktop CPU at interactive rates (27-30 frames per
second). To the best of our knowledge, our approach is 4-5 times faster than
prior methods, which provide similar accuracy
SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth
Exploring robust and efficient association methods has always been an
important issue in multiple-object tracking (MOT). Although existing tracking
methods have achieved impressive performance, congestion and frequent
occlusions still pose challenging problems in multi-object tracking. We reveal
that performing sparse decomposition on dense scenes is a crucial step to
enhance the performance of associating occluded targets. To this end, we
propose a pseudo-depth estimation method for obtaining the relative depth of
targets from 2D images. Secondly, we design a depth cascading matching (DCM)
algorithm, which can use the obtained depth information to convert a dense
target set into multiple sparse target subsets and perform data association on
these sparse target subsets in order from near to far. By integrating the
pseudo-depth method and the DCM strategy into the data association process, we
propose a new tracker, called SparseTrack. SparseTrack provides a new
perspective for solving the challenging crowded scene MOT problem. Only using
IoU matching, SparseTrack achieves comparable performance with the
state-of-the-art (SOTA) methods on the MOT17 and MOT20 benchmarks. Code and
models are publicly available at \url{https://github.com/hustvl/SparseTrack}.Comment: 12 pages, 8 figure
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