26,570 research outputs found
Note: An object detection method for active camera
To solve the problems caused by a changing background during object detection in active camera, this paper proposes a new method based on SURF (speeded up robust features) and data clustering. The SURF feature points of each image are extracted, and each cluster center is calculated by processing the data clustering of k adjacent frames. Templates for each class are obtained by calculating the histograms within the regions around the center points of the clustering classes. The window of the moving object can be located by finding the region that satisfies the histogram matching result between adjacent frames. Experimental results demonstrate that the proposed method can improve the effectiveness of object detection.Yong Chen, Ronghua Zhang, Lei Shang, and Eric H
Real-time detection and tracking of multiple objects with partial decoding in H.264/AVC bitstream domain
In this paper, we show that we can apply probabilistic spatiotemporal
macroblock filtering (PSMF) and partial decoding processes to effectively
detect and track multiple objects in real time in H.264|AVC bitstreams with
stationary background. Our contribution is that our method cannot only show
fast processing time but also handle multiple moving objects that are
articulated, changing in size or internally have monotonous color, even though
they contain a chaotic set of non-homogeneous motion vectors inside. In
addition, our partial decoding process for H.264|AVC bitstreams enables to
improve the accuracy of object trajectories and overcome long occlusion by
using extracted color information.Comment: SPIE Real-Time Image and Video Processing Conference 200
Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd
Object detection and 6D pose estimation in the crowd (scenes with multiple
object instances, severe foreground occlusions and background distractors), has
become an important problem in many rapidly evolving technological areas such
as robotics and augmented reality. Single shot-based 6D pose estimators with
manually designed features are still unable to tackle the above challenges,
motivating the research towards unsupervised feature learning and
next-best-view estimation. In this work, we present a complete framework for
both single shot-based 6D object pose estimation and next-best-view prediction
based on Hough Forests, the state of the art object pose estimator that
performs classification and regression jointly. Rather than using manually
designed features we a) propose an unsupervised feature learnt from
depth-invariant patches using a Sparse Autoencoder and b) offer an extensive
evaluation of various state of the art features. Furthermore, taking advantage
of the clustering performed in the leaf nodes of Hough Forests, we learn to
estimate the reduction of uncertainty in other views, formulating the problem
of selecting the next-best-view. To further improve pose estimation, we propose
an improved joint registration and hypotheses verification module as a final
refinement step to reject false detections. We provide two additional
challenging datasets inspired from realistic scenarios to extensively evaluate
the state of the art and our framework. One is related to domestic environments
and the other depicts a bin-picking scenario mostly found in industrial
settings. We show that our framework significantly outperforms state of the art
both on public and on our datasets.Comment: CVPR 2016 accepted paper, project page:
http://www.iis.ee.ic.ac.uk/rkouskou/6D_NBV.htm
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
Human behavioural analysis with self-organizing map for ambient assisted living
This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints
FPGA-based Anomalous trajectory detection using SOFM
A system for automatically classifying the trajectory of a moving object in a scene as usual or suspicious is presented. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a reconfigurable hardware architecture (Field Programmable Gate Array) to cluster trajectories acquired over a period, in order to detect novel ones. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 15\% classification error, showing the robustness of our approach over others in literature and the speed-up over the use of conventional microprocessor as compared to the use of an off-the-shelf FPGA prototyping board
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