5,657 research outputs found
A Deep-structured Conditional Random Field Model for Object Silhouette Tracking
In this work, we introduce a deep-structured conditional random field
(DS-CRF) model for the purpose of state-based object silhouette tracking. The
proposed DS-CRF model consists of a series of state layers, where each state
layer spatially characterizes the object silhouette at a particular point in
time. The interactions between adjacent state layers are established by
inter-layer connectivity dynamically determined based on inter-frame optical
flow. By incorporate both spatial and temporal context in a dynamic fashion
within such a deep-structured probabilistic graphical model, the proposed
DS-CRF model allows us to develop a framework that can accurately and
efficiently track object silhouettes that can change greatly over time, as well
as under different situations such as occlusion and multiple targets within the
scene. Experiment results using video surveillance datasets containing
different scenarios such as occlusion and multiple targets showed that the
proposed DS-CRF approach provides strong object silhouette tracking performance
when compared to baseline methods such as mean-shift tracking, as well as
state-of-the-art methods such as context tracking and boosted particle
filtering.Comment: 17 page
Instance Flow Based Online Multiple Object Tracking
We present a method to perform online Multiple Object Tracking (MOT) of known
object categories in monocular video data. Current Tracking-by-Detection MOT
approaches build on top of 2D bounding box detections. In contrast, we exploit
state-of-the-art instance aware semantic segmentation techniques to compute 2D
shape representations of target objects in each frame. We predict position and
shape of segmented instances in subsequent frames by exploiting optical flow
cues. We define an affinity matrix between instances of subsequent frames which
reflects locality and visual similarity. The instance association is solved by
applying the Hungarian method. We evaluate different configurations of our
algorithm using the MOT 2D 2015 train dataset. The evaluation shows that our
tracking approach is able to track objects with high relative motions. In
addition, we provide results of our approach on the MOT 2D 2015 test set for
comparison with previous works. We achieve a MOTA score of 32.1
Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector
Multiple object tracking (MOT) in urban traffic aims to produce the
trajectories of the different road users that move across the field of view
with different directions and speeds and that can have varying appearances and
sizes. Occlusions and interactions among the different objects are expected and
common due to the nature of urban road traffic. In this work, a tracking
framework employing classification label information from a deep learning
detection approach is used for associating the different objects, in addition
to object position and appearances. We want to investigate the performance of a
modern multiclass object detector for the MOT task in traffic scenes. Results
show that the object labels improve tracking performance, but that the output
of object detectors are not always reliable.Comment: 13th International Symposium on Visual Computing (ISVC
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