97,846 research outputs found
Object tracking with background elimination
Humans can follow moving objects and grasp an object of interest starting from early childhood. To
grasp an object successfully, one needs to be able to recognize the object of interest against its background.
If the object is moving, it is also critical to realize that the same object may look different from different
perspectives. Then, an estimation of depth is required to decide if the object is within reach. Finally, when
ready to grasp the object, one needs to figure out the part of the object to be held and the gesture of
the hand to grasp the object. This process might be intuitive for humans, but it is not as simple for robots.
The purpose of this research is to program the robot so that it can behave like a child who would
follow moving objects unconsciously. Therefore, the thesis mainly focuses on the first step: object
recognition. To begin, a simple object detector using color detection and segmentation was implemented.
Next was experimentation with object tracking algorithms which perform feature matching based on feature
descriptors like SIFT or ORB. Last, the Haar Feature-based Cascade Classifier was used to look at the learning based method.
All these methods helped to understand how people approach the problem of object recognition,
but they either have substantial limitations or do not quite satisfy the purpose of the research. Therefore,
finally implemented was an object detection method by performing background elimination, which is able to
track an arbitrary number of moving objects fast.Ope
Text Extraction From Natural Scene: Methodology And Application
With the popularity of the Internet and the smart mobile device, there is an increasing demand for the techniques and applications of image/video-based analytics and information retrieval. Most of these applications can benefit from text information extraction in natural scene. However, scene text extraction is a challenging problem to be solved, due to cluttered background of natural scene and multiple patterns of scene text itself. To solve these problems, this dissertation proposes a framework of scene text extraction.
Scene text extraction in our framework is divided into two components, detection and recognition. Scene text detection is to find out the regions containing text from camera captured images/videos. Text layout analysis based on gradient and color analysis is performed to extract candidates of text strings from cluttered background in natural scene. Then text structural analysis is performed to design effective text structural features for distinguishing text from non-text outliers among the candidates of text strings. Scene text recognition is to transform image-based text in detected regions into readable text codes. The most basic and significant step in text recognition is scene text character (STC) prediction, which is multi-class classification among a set of text character categories. We design robust and discriminative feature representations for STC structure, by integrating multiple feature descriptors, coding/pooling schemes, and learning models. Experimental results in benchmark datasets demonstrate the effectiveness and robustness of our proposed framework, which obtains better performance than previously published methods.
Our proposed scene text extraction framework is applied to 4 scenarios, 1) reading print labels in grocery package for hand-held object recognition; 2) combining with car detection to localize license plate in camera captured natural scene image; 3) reading indicative signage for assistant navigation in indoor environments; and 4) combining with object tracking to perform scene text extraction in video-based natural scene. The proposed prototype systems and associated evaluation results show that our framework is able to solve the challenges in real applications
Deep-LK for Efficient Adaptive Object Tracking
In this paper we present a new approach for efficient regression based object
tracking which we refer to as Deep- LK. Our approach is closely related to the
Generic Object Tracking Using Regression Networks (GOTURN) framework of Held et
al. We make the following contributions. First, we demonstrate that there is a
theoretical relationship between siamese regression networks like GOTURN and
the classical Inverse-Compositional Lucas & Kanade (IC-LK) algorithm. Further,
we demonstrate that unlike GOTURN IC-LK adapts its regressor to the appearance
of the currently tracked frame. We argue that this missing property in GOTURN
can be attributed to its poor performance on unseen objects and/or viewpoints.
Second, we propose a novel framework for object tracking - which we refer to as
Deep-LK - that is inspired by the IC-LK framework. Finally, we show impressive
results demonstrating that Deep-LK substantially outperforms GOTURN.
Additionally, we demonstrate comparable tracking performance to current state
of the art deep-trackers whilst being an order of magnitude (i.e. 100 FPS)
computationally efficient
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
DART: Distribution Aware Retinal Transform for Event-based Cameras
We introduce a generic visual descriptor, termed as distribution aware
retinal transform (DART), that encodes the structural context using log-polar
grids for event cameras. The DART descriptor is applied to four different
problems, namely object classification, tracking, detection and feature
matching: (1) The DART features are directly employed as local descriptors in a
bag-of-features classification framework and testing is carried out on four
standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS,
NCaltech-101). (2) Extending the classification system, tracking is
demonstrated using two key novelties: (i) For overcoming the low-sample problem
for the one-shot learning of a binary classifier, statistical bootstrapping is
leveraged with online learning; (ii) To achieve tracker robustness, the scale
and rotation equivariance property of the DART descriptors is exploited for the
one-shot learning. (3) To solve the long-term object tracking problem, an
object detector is designed using the principle of cluster majority voting. The
detection scheme is then combined with the tracker to result in a high
intersection-over-union score with augmented ground truth annotations on the
publicly available event camera dataset. (4) Finally, the event context encoded
by DART greatly simplifies the feature correspondence problem, especially for
spatio-temporal slices far apart in time, which has not been explicitly tackled
in the event-based vision domain.Comment: 12 pages, revision submitted to TPAMI in Nov 201
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