Multiple Object Detection Research Articles

CW and Pulse–Doppler Radar Processing Based on FPGA for Human Sensing Applications

In this paper, we discuss using field-programmable gate arrays (FPGAs) to process either time- or frequency-domain signals in human sensing radar applications. One example will be given for a continuous-wave (CW) Doppler radar and another for an ultrawideband (UWB) pulse–Doppler (PD) radar. The example for the CW Doppler radar utilizes a novel superheterodyne receiver to suppress low-frequency noise and includes a digital downconverter module implemented in an FPGA. Meanwhile, the UWB PD radar employs a carrier-based transceiver and a novel equivalent time sampling scheme based on FPGA for narrow pulse digitization. Highly integrated compact data acquisition hardware has been implemented and exploited in both radar prototypes. Typically, the CW Doppler radar is a low-cost option for single human activity monitoring, vital sign detection, etc., where target range information is not required. Meanwhile, the UWB PD radar is more advanced in through-wall sensing, multiple-object detection, real-time target tracking, and so on, where a high-resolution range profile is acquired together with a micro-Doppler signature. Design challenges, performance comparison, pros, and cons will be discussed in detail.

Bounding Box Object Localization Based On Image Superpixelization

One of successful approaches for object localization and recognition is sliding window approach where different candidate windows are found and evaluated and the best window is selected to represent the object. To avoid exhaustive search over all windows locations, Efficient Sub-window Search (ESS) [1] algorithm were proposed to efficiently find the best window among all sub-windows using branch and bound technique. In case of multi-class multiple object detection problems, such methods are time consuming. To handle this issue, efficient multi-object detection approach is proposed based on image superpixelization We utilize image superpixels in 2 points. (a) Such preprocessing stage is done once for an image, hence multiple detections could be fast. (b) We observe that image superpixels could help in identifying the promising candidate sub-windows, hence evaluating all sub-windows could be avoided. An efficient brute force sub-window search algorithm is proposed based on these observations. Moreover, for improving its performance, the algorithm is integrated with ESS algorithm. The proposed algorithms are assessed on the PASCAL 2006 and PASCAL 2007 test-set and show that they are faster than the state-of-the art sub-window search algorithms, while achieving a comparable performance comparing with them.

Visual Attention Model with Adaptive Weighting of Conspicuity Maps for Building Detection in Satellite Images

Abstract The lack of automation and the limited performance of current image processing techniques pose critical challenges to the efficient and timely use of the large amount of data made available by aerial and space based assets. The imitation of fast adaptation and inference capability of human visual system appears to be a promising research direction for the development of computational algorithms able to deal with large variations in image content, characteristics and scale as those encountered in satellite imaging. The paper explores the potential use of an improved computational model of visual attention for the complex task of building identification in satellite images. It contributes to extend the envelope of application areas of such models and also to expand their current use from single object to multiple object detection. A set of original weighting schemes based on the contribution of different features to the identification of building and non-building areas is first proposed and evaluated against existing solutions in the literature. A novel adaptive algorithm then chooses the best weighting scheme based on a similarity error to ensure the best performance of the attention model in a given context. Finally, a neural network is trained to predict the set of weights provided by the best weighting scheme for the context of the image in which buildings are to be detected. The solution provides encouraging results on a set of 50 satellite images.

Security System and Surveillance Using Real Time Object Tracking and Multiple Cameras

In this paper we propose multiple cameras using real time tracking for surveillance and security system. It is extensively used in the research field of computer vision applications, like that video surveillance, authentication systems, robotics, pre-stage of MPEG4 image compression and user inter faces by gestures. The key components of tracking for surveillance system are extracting the feature, background subtraction and identification of extracted object. Video surveillance, object detection and tracking have drawn a successful increased interest in recent years. A object tracking can be understood as the problem of finding the path (i.e. trajectory) and it can be defined as a procedure to identify the different positions of the object in each frame of a video. Based on the previous work on single detection using single stationary camera, we extend the concept to enable the tracking of multiple object detection under multiple camera and also maintain a security based system by multiple camera to track person in indoor environment, to identify by my proposal system which consist of multiple camera to monitor a person. Present study mainly aims to provide security and detect the moving object in real time video sequences and live video streaming. Based on a robust algorithm for human body detection and tracking in videos created with support of multiple cameras.

Realizing real‐time centroid detection of multiple objects with marching pixels algorithms on programmable customizing hardware

SUMMARYIn this paper, we present a class of emergent algorithms called Marching Pixels and a corresponding programmable parallel chip architecture. Marching Pixels can be used for real‐time image processing in smart camera chips. They are based on hardware agents, which are virtually crawling in a pixel grid image to find attributes like centroid, rotation, and size of an arbitrary number of objects given in an image. Because of the distributed and local processing scheme of Marching Pixels, reply times in milliseconds can be fulfilled. This means that time is determined where pre‐known objects are located and how they are oriented to the main axes of the image. We present an example Marching Pixels algorithm and corresponding application‐specific and programmable parallel architectures. The latter contains a specific instruction set that allows not only the execution of Marching Pixels algorithms but also of arbitrary Cellular Automata algorithms as an embedded parallel processor. The strengths and weaknesses of this architecture concerning the realization as field‐programmable gate arrays and application‐specific integrated circuits are discussed by means of hardware synthesis results. These results are compared with the solution achievable on a real hardware like the Atom processor. Copyright © 2011 John Wiley & Sons, Ltd.

Semi-Automatic segmentation of multiple mouse embryos in MR images

BackgroundThe motivation behind this paper is to aid the automatic phenotyping of mouse embryos, wherein multiple embryos embedded within a single tube were scanned using Magnetic Resonance Imaging (MRI).ResultsOur algorithm, a modified version of the simplex deformable model of Delingette, addresses various issues with deformable models including initialization and inability to adapt to boundary concavities. In addition, it proposes a novel technique for automatic collision detection of multiple objects which are being segmented simultaneously, hence avoiding major leaks into adjacent neighbouring structures. We address the initialization problem by introducing balloon forces which expand the initial spherical models close to the true boundaries of the embryos. This results in models which are less sensitive to initial minimum of two fold after each stage of deformation. To determine collision during segmentation, our unique collision detection algorithm finds the intersection between binary masks created from the deformed models after every few iterations of the deformation and modifies the segmentation parameters accordingly hence avoiding collision.We have segmented six tubes of three dimensional MR images of multiple mouse embryos using our modified deformable model algorithm. We have then validated the results of the our semi-automatic segmentation versus manual segmentation of the same embryos. Our Validation shows that except paws and tails we have been able to segment the mouse embryos with minor error.ConclusionsThis paper describes our novel multiple object segmentation technique with collision detection using a modified deformable model algorithm. Further, it presents the results of segmenting magnetic resonance images of up to 32 mouse embryos stacked in one gel filled test tube and creating 32 individual masks.

Radar‐based perception for autonomous outdoor vehicles

AbstractAutonomous vehicle operations in outdoor environments challenge robotic perception. Construction, mining, agriculture, and planetary exploration environments are examples in which the presence of dust, fog, rain, changing illumination due to low sun angles, and lack of contrast can dramatically degrade conventional stereo and laser sensing. Nonetheless, environment perception can still succeed under compromised visibility through the use of a millimeter‐wave radar. Radar also allows for multiple object detection within a single beam, whereas other range sensors are limited to one target return per emission. However, radar has shortcomings as well, such as a large footprint, specularity effects, and limited range resolution, all of which may result in poor environment survey or difficulty in interpretation. This paper presents a novel method for ground segmentation using a millimeter‐wave radar mounted on a ground vehicle. Issues relevant to short‐range perception in an outdoor environment are described along with field experiments and a quantitative comparison to laser data. The ability to classify the ground is successfully demonstrated in clear and low‐visibility conditions, and significant improvement in range accuracy is shown. Finally, conclusions are drawn on the utility of millimeter‐wave radar as a robotic sensor for persistent and accurate perception in natural scenarios. © 2011 Wiley Periodicals, Inc.

The MOPED framework: Object recognition and pose estimation for manipulation

We present MOPED, a framework for Multiple Object Pose Estimation and Detection that seamlessly integrates single-image and multi-image object recognition and pose estimation in one optimized, robust, and scalable framework. We address two main challenges in computer vision for robotics: robust performance in complex scenes, and low latency for real-time operation. We achieve robust performance with Iterative Clustering Estimation (ICE), a novel algorithm that iteratively combines feature clustering with robust pose estimation. Feature clustering quickly partitions the scene and produces object hypotheses. The hypotheses are used to further refine the feature clusters, and the two steps iterate until convergence. ICE is easy to parallelize, and easily integrates single- and multi-camera object recognition and pose estimation. We also introduce a novel object hypothesis scoring function based on M-estimator theory, and a novel pose clustering algorithm that robustly handles recognition outliers. We achieve scalability and low latency with an improved feature matching algorithm for large databases, a GPU/CPU hybrid architecture that exploits parallelism at all levels, and an optimized resource scheduler. We provide extensive experimental results demonstrating state-of-the-art performance in terms of recognition, scalability, and latency in real-world robotic applications.

Neural generators underlying concurrent sound segregation

Although an object-based account of auditory attention has become an increasingly popular model for understanding how temporally overlapping sounds are segregated, relatively little is known about the cortical circuit that supports such ability. In the present study, we applied a beamformer spatial filter to magnetoencephalography (MEG) data recorded during an auditory paradigm that used inharmonicity to promote the formation of multiple auditory objects. Using this unconstrained, data-driven approach, the evoked field component linked with the perception of multiple auditory objects (i.e., the object-related negativity; ORNm), was found to be associated with bilateral auditory cortex sources that were distinct from those coinciding with the P1m, N1m, and P2m responses elicited by sound onset. The right hemispheric ORNm source in particular was consistently positioned anterior to the other sources across two experiments. These findings are consistent with earlier proposals of multiple auditory object detection being associated with generators in the auditory cortex and further suggest that these neural populations are distinct from the long latency evoked responses reflecting the detection of sound onset.

A Study on Software-based Sensing Technology for Multiple Object Control in AR Video

Researches on Augmented Reality (AR) have recently received attention. With these, the Machine-to-Machine (M2M) market has started to be active and there are numerous efforts to apply this to real life in all sectors of society. To date, the M2M market has applied the existing marker-based AR technology in entertainment, business and other industries. With the existing marker-based AR technology, a designated object can only be loaded on the screen from one marker and a marker has to be added to load on the screen the same object again. This situation creates a problem where the relevant marker’should be extracted and printed in screen so that loading of the multiple objects is enabled. However, since the distance between markers will not be measured in the process of detecting and copying markers, the markers can be overlapped and thus the objects would not be augmented. To solve this problem, a circle having the longest radius needs to be created from a focal point of a marker to be copied, so that no object is copied within the confines of the circle. In this paper, software-based sensing technology for multiple object detection and loading using PPHT has been developed and overlapping marker control according to multiple object control has been studied using the Bresenham and Mean Shift algorithms.

Joint Detection and Estimation of Multiple Objects From Image Observations

The problem of jointly detecting multiple objects and estimating their states from image observations is formulated in a Bayesian framework by modeling the collection of states as a random finite set. Analytic characterizations of the posterior distribution of this random finite set are derived for various prior distributions under the assumption that the regions of the observation influenced by individual objects do not overlap. These results provide tractable means to jointly estimate the number of states and their values from image observations. As an application, we develop a multi-object filter suitable for image observations with low signal-to-noise ratio (SNR). A particle implementation of the multi-object filter is proposed and demonstrated via simulations.

A General Framework for Multi-Human Tracking

The task of reliable detection and tracking of multiple objects becomes highly complex for crowded scenarios. In this paper, a robust framework is presented for multi-Human tracking. The key contribution of the work is to use fast calculation for mean shift algorithm to perform tracking for the cases when Kalman filter fails due to measurement error. Local density maxima in the difference image - usually representing moving objects - are outlined by a fast non-parametric mean shift clustering procedure. The proposed approach has the robust ability to track moving objects, both separately and in groups, in consecutive frames under some kinds of difficulties such as rapid appearance changes caused by image noise and occlusion.

Object tracking using AM-FM image features

AM-FM models analyse an image in terms of amplitude (AM) and frequency modulated (FM) sinusoids. In this study, the authors present detection and tracking of single and multiple objects in video sequences using AM-FM features. The authors use the particle filtering framework for estimating the motion parameters. The single object tracking algorithm uses an affine motion model and a subspace-based appearance model. The multiple object tracking algorithm, which is a logical extension of single object tracking, can handle varying number of interacting objects. The performance of both single and multiple object tracking is illustrated on real-world videos.

A General Framework for Multi-Human Tracking using Kalman Filter and Fast Mean Shift Algorithms

The task of reliable detection and tracking of multiple objects becomes highly complex for crowded scenarios. In this paper, a robust framework is presented for multi-Human tracking. The key contribution of the work is to use fast calculation for mean shift algorithm to perform tracking for the cases when Kalman filter fails due to measurement error. Local density maxima in the difference image - usually representing moving objects - are outlined by a fast non-parametric mean shift clustering procedure. The proposed approach has the robust ability to track moving objects, both separately and in groups, in consecutive frames under some kinds of difficulties such as rapid appearance changes caused by image noise and occlusion.

Multiple object detection and tracking with pseudo-particle filter

To tackle the divergence of the classical particle filter method for multiple object tracking in image sequences, a new particle filter, called pseudoparticle filter (PPF), is proposed. The PPF invokes subset particles of generic particle filters to form a continuous estimate of the posterior density function of the objects. After sampling-importance resampling (SIR), the subset particles converge to the observations. It is proved that, using an appropriate kernel function of the mean shift algorithm, we can get the subset particles of the observations and the fixed points of clustering results as the state of the objects. A multiple object data association and state estimation technique is proposed to resolve the subset particles correspondence ambiguities that arise when multiple objects are present. Experimental results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking.

A TEAM OF HUMANOID GAME COMMENTATORS

We present a team of two humanoid robot commentators for AIBO robot soccer games. The two humanoids stand by the side lines of the playing field, autonomously observe the game, wirelessly listen to a "game controller" computer, recognize events, and select announcing actions that may require coordination with each other. Given the large degree of uncertainty and dynamics of the robot soccer games, we further introduce a "Puppet Master" control that allows humans to intervene, prompting the robots to commentate an event if previously undefined or undetected. The robots recognize events based on input from these three sources, namely own and shared vision, game controller, and occasional Puppet Master. We present the two-humanoid behavioral architecture and the vision-based event recognition, including a SIFT-based vision processing algorithm that allows for the detection of multiple similar objects, such as the identical shaped robot players. We introduce the commentating algorithm that probabilistically selects a commentating action from a set of weighted actions corresponding to a detected event. The probabilistic selection uses the game history and updates the action weights to effectively avoid repetition of comments to enable entertainment. Our work, corresponding to a fully implemented system, CMCast, with two QRIO robots, contributes a team of two humanoids fully executing a challenging observation, modeling, coordination, and reporting task.

JFEA_49_2_abstracts

In the past decade, contrast features, which focus on rarity and uniqueness, have been widely used in the saliency-detection field, but the extreme dependency on the most-highlighted region remains as a limitation for the detection of multiple and complex objects. In particular, the difficulties are commonly observed when a high inter-object dissimilarity exists. Based on this observation, we present a new paradigm for the detection of the salient-object region, whereby only the spatial-saliency clues are interpreted from a multiple-level clustering framework; for this reason, in contrast to the previous methods, the proposed model is not dependent upon the contrast features. The proposed model can be roughly decomposed into the following four main phases: regional feature extraction, homogeneous-region clustering, saliency-score computation, and recursive processing. In particular, a recursive processing for which the salient region is optimized through the improvement of its clustering results is introduced. According to the experiment results, the proposed scheme outperforms the state-of-the-art methods on various benchmark datasets consisting of single, multiple, and complex object images; furthermore, the proposed model is more effective for the detection of multiple objects. To validate the contributions of this study, a multiple salient-object dataset (MSOD) that contains 100 images with more than two objects with a higher dissimilarity was also constructed.

Improved immunity to lift-off effect in pulsed eddy current testing with two-stage differential probes

Pulse eddy current (PEC) testing is broadly used as an effective nondestructive testing technique especially for the defect detection of multiple layer and irregular surface objects. PEC testing’s adoption of repeated pulses as excitation provides for the penetration of many frequency components into the piece being tested and returns more information from various depths. As the lift-off height will inevitably change during detection, the lift-off effect serves as a significant obstacle to implement effective nondestructive testing and further evaluations. This paper concentrates on improving the probe’s immunity to the lift-off effect and proposes two-stage PEC differential probes. Physical experiments are addressed in this paper. Experimental results demonstrate that two-stage differential PEC probes are able to suppress lift-off effects.

A Variational Shape Optimization Approach for Image Segmentation with a Mumford–Shah Functional

We introduce a novel computational method for a Mumford–Shah functional, which decomposes a given image into smooth regions separated by closed curves. Casting this as a shape optimization problem, we develop a gradient descent approach at the continuous level that yields nonlinear PDE flows. We propose time discretizations that linearize the problem and space discretization by continuous piecewise linear finite elements. The method incorporates topological changes, such as splitting and merging for detection of multiple objects, space–time adaptivity, and a coarse-to-fine approach to process large images efficiently. We present several simulations that illustrate the performance of the method and investigate the model sensitivity to various parameters.

A unified perspective and new results on RHT computing, mixture based learning, and multi-learner based problem solving

On one hand, multiple object detection approaches of Hough transform (HT) type and randomized HT type have been extended into an evidence accumulation featured general framework for problem solving, with five key mechanisms elaborated and several extensions of HT and RHT presented. On the other hand, another framework is proposed to integrate typical multi-learner based approaches for problem solving, particularly on Gaussian mixture based data clustering and local subspace learning, multi-sets mixture based object detection and motion estimation, and multi-agent coordinated problem solving. Typical learning algorithms, especially those that base on rival penalized competitive learning (RPCL) and Bayesian Ying–Yang (BYY) learning, are summarized from a unified perspective with new extensions. Furthermore, the two different frameworks are not only examined with one viewed crossly from a perspective of the other, with new insights and extensions, but also further unified into a general problem solving paradigm that consists of five basic mechanisms in terms of acquisition, allocation, amalgamation, admission, and affirmation, or shortly A5 paradigm.