38,924 research outputs found
DALES: Automated Tool for Detection, Annotation, Labelling and Segmentation of Multiple Objects in Multi-Camera Video Streams
In this paper, we propose a new software tool called DALES to extract semantic information
from multi-view videos based on the analysis of their visual content. Our system is fully automatic
and is well suited for multi-camera environment. Once the multi-view video sequences are
loaded into DALES, our software performs the detection, counting, and segmentation of the visual
objects evolving in the provided video streams. Then, these objects of interest are processed
in order to be labelled, and the related frames are thus annotated with the corresponding semantic
content. Moreover, a textual script is automatically generated with the video annotations.
DALES system shows excellent performance in terms of accuracy and computational speed and
is robustly designed to ensure view synchronization
Unsupervised Object Discovery and Tracking in Video Collections
This paper addresses the problem of automatically localizing dominant objects
as spatio-temporal tubes in a noisy collection of videos with minimal or even
no supervision. We formulate the problem as a combination of two complementary
processes: discovery and tracking. The first one establishes correspondences
between prominent regions across videos, and the second one associates
successive similar object regions within the same video. Interestingly, our
algorithm also discovers the implicit topology of frames associated with
instances of the same object class across different videos, a role normally
left to supervisory information in the form of class labels in conventional
image and video understanding methods. Indeed, as demonstrated by our
experiments, our method can handle video collections featuring multiple object
classes, and substantially outperforms the state of the art in colocalization,
even though it tackles a broader problem with much less supervision
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