42,269 research outputs found
Smart Camera for MPEG-7
While a first generation of video coding techniques proposed to remove the redundancies in and between image frames to get smaller bitstreams, second generation schemes like MPEG-4 and MPEG-7 aim at doing content-based coding and interactivity. To reach this goal, tools for the extraction and description of semantic objects need to be developed. In this work, we propose an algorithm for the extraction and tracking of semantic objects and an MPEG-7 compliant descriptor set for generic objects; together, they can be seen like a smart camera for automatic scene description. Some parts of the proposed system will be tested by software. The tracking algorithm has been laid out so as to follow generic objects in scenes including partial occlusions and merging. To do this, we first localize each moving object of the scene using a change-detection mask. Then, a certain number of representative points called centroids is given to the objects by a fuzzy C-means algorithm. For each centroid of some current frame, we try to find the closest centroid in the previous frame. Once we found these pairs, each object can be labelled according to its corresponding previous centroids. The description structure is a subset of the DDL language used in MPEG-7. The main concern was to find a simple, but flexible descriptor set for generic objects. A corresponding C-structure for software implementations is also proposed and partially tested
Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking
The most common paradigm for vision-based multi-object tracking is
tracking-by-detection, due to the availability of reliable detectors for
several important object categories such as cars and pedestrians. However,
future mobile systems will need a capability to cope with rich human-made
environments, in which obtaining detectors for every possible object category
would be infeasible. In this paper, we propose a model-free multi-object
tracking approach that uses a category-agnostic image segmentation method to
track objects. We present an efficient segmentation mask-based tracker which
associates pixel-precise masks reported by the segmentation. Our approach can
utilize semantic information whenever it is available for classifying objects
at the track level, while retaining the capability to track generic unknown
objects in the absence of such information. We demonstrate experimentally that
our approach achieves performance comparable to state-of-the-art
tracking-by-detection methods for popular object categories such as cars and
pedestrians. Additionally, we show that the proposed method can discover and
robustly track a large variety of other objects.Comment: ICRA'18 submissio
Object Detection in Videos with Tubelet Proposal Networks
Object detection in videos has drawn increasing attention recently with the
introduction of the large-scale ImageNet VID dataset. Different from object
detection in static images, temporal information in videos is vital for object
detection. To fully utilize temporal information, state-of-the-art methods are
based on spatiotemporal tubelets, which are essentially sequences of associated
bounding boxes across time. However, the existing methods have major
limitations in generating tubelets in terms of quality and efficiency.
Motion-based methods are able to obtain dense tubelets efficiently, but the
lengths are generally only several frames, which is not optimal for
incorporating long-term temporal information. Appearance-based methods, usually
involving generic object tracking, could generate long tubelets, but are
usually computationally expensive. In this work, we propose a framework for
object detection in videos, which consists of a novel tubelet proposal network
to efficiently generate spatiotemporal proposals, and a Long Short-term Memory
(LSTM) network that incorporates temporal information from tubelet proposals
for achieving high object detection accuracy in videos. Experiments on the
large-scale ImageNet VID dataset demonstrate the effectiveness of the proposed
framework for object detection in videos.Comment: CVPR 201
A video object generation tool allowing friendly user interaction
In this paper we describe an interactive video object segmentation tool developed in the framework of the ACTS-AC098 MOMUSYS project. The Video Object Generator with User Environment (VOGUE) combines three different sets of automatic and semi-automatic-tool (spatial segmentation, object tracking and temporal segmentation) with general purpose tools for user interaction. The result is an integrated environment allowing the user-assisted segmentation of any sort of video sequences in a friendly and efficient manner.Peer ReviewedPostprint (published version
Lucid Data Dreaming for Video Object Segmentation
Convolutional networks reach top quality in pixel-level video object
segmentation but require a large amount of training data (1k~100k) to deliver
such results. We propose a new training strategy which achieves
state-of-the-art results across three evaluation datasets while using 20x~1000x
less annotated data than competing methods. Our approach is suitable for both
single and multiple object segmentation. Instead of using large training sets
hoping to generalize across domains, we generate in-domain training data using
the provided annotation on the first frame of each video to synthesize ("lucid
dream") plausible future video frames. In-domain per-video training data allows
us to train high quality appearance- and motion-based models, as well as tune
the post-processing stage. This approach allows to reach competitive results
even when training from only a single annotated frame, without ImageNet
pre-training. Our results indicate that using a larger training set is not
automatically better, and that for the video object segmentation task a smaller
training set that is closer to the target domain is more effective. This
changes the mindset regarding how many training samples and general
"objectness" knowledge are required for the video object segmentation task.Comment: Accepted in International Journal of Computer Vision (IJCV
- …