Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg