Building and improving upon the work of Petersson et al., Ekvall et al.  and Lopez et al.  decompose the object search problem into global and local search stages. Their coarse global search employed Receptive Field Coocurrence Histograms  to identify potential object locations. A mobile robot equipped with laser, sonar, and a pan-tilt-zoom camera then zooms into each hypothesized location to apply a localized object search algorithm (based on SIFT features). An a priori map built via SLAM is used to establish likely locations of known objects. Navigation is restricted to planning over a graph of predetermined free-space nodes. This approach simplies the methods of  and  and allows for simultaneous search of multiple objects. However, their approach is limited to planar objects whose pose is crudely approximated by a single laser scan point in  and later moderately rened in  to a distance measure based on comparing the number of occupied pixels in the image against a reference image. Furthermore, much prior information is assumed given or computed o-line (e.g., the SLAM-based map and the set of navigation nodes). The contributions of chapter 6 will improve on the work of  by using a 3D object detector and will also simplify the method of  by replacing the computationally expensive 3D visibility map and rating function with a global and local search technique that updates the grid-based probability map incorporated from .
and  use low-resolution time of-flight data to fix discontinuities in the high- resolution depth data acquired from a stereo camera setup.  fused time-of- flight and color data to segment the background in a video sequence, though the performance was about 10 frames per second.  fused laser range and color data to train a robot vision system. In , local appearance features extracted from color data are fused with stereo depth data in tracking of objects. Haar-like features detected in the color data are used to improve noise and low-confidence regions inherent in depth data produced from stereo cameras. However, the al- gorithm runs at 15Hz on a powerful PC due to the amount of computation necessary for extracting the appearance models. In , two separate particle filter trackers, one using color and the other using time-of-flight data, are de- scribed and compared on a variety of video sequences. Again, the approach for both trackers is not suitable for realtime as it involves heavy processing. The paper concludes that each performs better in different environments and that combining the two would be beneficial.
The positioning device is fabricated by three orthogonally- placed translational stages LX30, LX26 and LX20 (Misumi Group Inc., Tokyo, Japan). The three translational stages enable movement with an effective span of, 206 mm, 173 mm, and 73 mm in x-, y- and z-axes, respectively. Each stage is actuated by an ECMax22 motor (Table III) with a GP32/22 gear head (Table IV) (Maxon Motor, Sachseln, Switzerland), which is velocity controlled by an Elmo Whistle 2.5/60 motor controller (Elmo Motion Control Ltd, Petach-Tikva, Israel). The positioning accuracy of the linear stages are given in Table II. If we assume torsional stiffness of the gear head, the positioning accuracy of the gear head could be determined from the gear backlash (Table IV). By combining the inaccuracies introduced by the linear stage and the gear head, the positioning accuracy of the complete positioning device can be determined. The positioning accuracy of the positioning device is determined at 27 µm, 35 µm and 41 µm along the x-, y- and z-axes, respectively. In order to pro- vide consistent measurements, the ultrasound transducer is clamped at the end-effector in a fixed pose using a special designed clamp. A 3D scan of the ultrasound transducer was made using a Vivid 910 3D laser scanner (Konica Minolta Sensing, Inc., Tokyo, Japan). The 3D scan is used to design a transducer clamp capable of fixing the transducer in a fixed pose at end-effector of the positioning device. The designed clamp is 3D printed using a Eden 250 3D printer (Objet Ltd., Rehovot, Israel) and mounted at the positioning device end-effector.
Initially vehicle tracking systems were developed for fleet management. It was a passive tracking system, a hardware device installed in the vehicle that stores GPS location, speed, heading and a trigger event such as key on/off, door open/closed etc,. When vehicle returns to a specific location device is removed and data downloaded to computer. Passive systems also included auto download type that transfer data via wireless download but were not realtime. Passive systems did not serve much useful to track consumer’s vehicle for theft prevention. Realtimetracking system was required that can transmit the collected information about the vehicle after regular intervals or at least could transmit the information when required by monitoring station. This led to the development of active systems.
We can see in Fig.1, most of the contrasted discriminative methods depend on single feature (via feature selection such as online AdaBoost method (OAB) ) or strong classifiers (SVM classifier such as Struck (Struck) method , and Tracking-Learning-Detection (TLD) ) for target tracking. The OAB and Struck methods do not perform well on this sequence as these methods use single features which are less effective for large scale pose variations. All those trackers showed significant drift when the target undergoing heavy pose variation (#3845) and does not re-detect targets in the case of tracking failure. Although, the TLD tracker is able to re-detect target objects which is similar to our proposed method in the case of losing the target. It does not follow targets in #3965 accurately due to the unconstrained false target re-detection. However, our method (LKCF) tracker is not having drift even in the background noise scene (#4181) when the TLD having lost the target. The LKCF successfully completed until the end of the sequence. Fig.2 shows that the OAB method failed near frame 520 for our testing video and struck failed near frame 640 which both perform well in short-term sequences. The TLD method tracking length is longer than those because it maintains a detector during tracking. In those comparisons our method was able to maintain tracks against existing trackers. Note that our tracker drifts in the 1780th and 2000th frames separately due to large scale pose variations, but manages to re-detect the targets subsequently in a short period.
Delivery of products ordered online demands more employees for the delivery purposes. Also there are many areas wherein people are more involved in field work away from their office premises. They may try to find shortcuts in completing the work or leaving the field of work before the expected time. Complaints can be registered against such acts. However, we cannot just believe in the complaints registered. We need some proof showing the complaint registered is a genuine one. So if we have the data of the employee's previous location stored with us, we can check if the complaint is true and accordingly take action which will contribute to customer usability. Also the head person may at any time feel the need to check where the employee at that particular time is or payment on hourly basis, are some of the uses of the proposed application - RealTimeTracking System.
Our problem setting and requirements differ from those of the existing methods in several respects. First, emerging significant clusters need to be detected in realtime and immediately shown to the observer, permitting no reliance on off-line post- processing. Second, clusters may emerge, evolve (grow, shrink, move, change shape, split, merge), and disappear, excluding the approaches assuming a constant number of clusters, like . Third, the main memory limitations is not our primary focus. We assume that the available memory is sufficient for keeping all micro-clusters that may co-exist within a certain time interval T. Fourth, old micro-clusters (where the latest event is older than T) are not of interest anymore and may be discarded.
due to various reasons. Most people complained missing the bus by a small margin, they spent over 20 minutes waiting at the bus stop and suggested that it would be beneficial if they received realtime updates to the bus locations. The vehicle tracking system that is developed addresses all these issues and makes use of commonly available smartphones that are already owned by most of the individuals. This reduces a major share of the investment cost to setup the system. The system that is proposed addresses an issue that is faced globally. The system would provide the public with real-time location updates of the buses that they intend to board. It also gives them a list of all the instances of the buses currently running, the distance of the bus from the user, and the estimated arrival time of the bus to where the user is. The user can see the bus on a map too. In this way the user can save a lot of time daily. It also makes the public bus transport system more efficient and reliable. It will definitely make the current system more advanced and would lead to an increase in the number of people preferring buses for their means of transport. The use of buses is spread worldwide and it is essential that its standard be raised so that it becomes the most efficient and fastest means of transportation for one and all.
The methods could then be divided in different categories. The first one deals with a 3D parametric model and the tracking is defined as a minimization prob- lem where a cost function is minimised to obtained the best parameter values that make the model pose match with the hand one. The cost function is also called a dissimilarity function where extracted features from hand images and model ones are used to compare the poses from real hand and 3D model. The dissimilarity function is minimized by means of minimsation algorithm such as the simplex approach proposed by Nelder and Mead  or using statistic meth- ods such as particle filter . The second category is mostly called data-driven method and uses a database of gestures calculated before the tracking process. It consists of matching the real hand pose with ones stored in the database through regression or classification techniques. The author used coloured glove to im- prove the matching between input images and the ones stored in a database of images. The most important problem with this kind of methods is the huge number of hand gestures which makes impossible to create a database with all hand poses. In general a limited number of gestures is used to create a realtime application.
The version of this template is V2. Most of the formatting instructions in this document have been compiled by Causal Productions from the IEEE LaTeX style files. Causal Productions offers both A4 templates and US Letter templates for LaTeX and Microsoft Word. The LaTeX templates depend on the official IEEEtran.cls and IEEEtran.bst files, whereas the Microsoft Word templates are self-contained. Causal Productions has used its best efforts to ensure that the templates have the same appearance.This is the important part in visualizing the scene with the realtime data. The vega prime tool supports us in creating the scenario and delivering the data. The vega prime call function which enables to load the real-time scenario, with this scenario and terrain model the 3D model flight is generated with actual present live flight data. The real happening in the outer space is visually driven in the ATC tower. After integrating the models with the real-time ADS-B network data the 3D model libraries are called according to the aircraft take-off and landings. The exact flight models can be integrated with the data, after further programming and creating the whole world flight data. The 3D models will be called by a simple program. All the scene models are driven after calling upon the scene libraries. This function is enabled by the preprogrammed vpapp. The
There are several requirements should be considered in designing this system. First, this tracking algorithm must be robust, which can easily detect and locate the target, and adapt many complicated environment without loss the target; second, this high demand of real-time capability to fulfil the task. Third, the payload of the luggage delivery robot is much higher compared to home-service robots. Finally, the interface of system is friendly and users can operate easily and expediently. In this study, we mainly focus on the selection and modification of tracking algorithm which can satisfy the practical application needs.
Moving Object Tracking using Optical Flow and Motion Vector Estimation  that moving object detection and tracking is an developing exploration field since it has vast applications in traffic inspection, 3D observation, movement investigation (human and non-human), activity recognition, therapeutic imaging etc. designing a modern object perception and tracking algorithm which utilized optical flow belongs to motion vector estimation for object detection and tracking in a successive frames. The optical flow gives significant information about the object movement regardless of whether no quantitative parameters are processed. The motion vector estimation strategy can provide an estimation of object position from successive frames which built the exactness of this algorithm and helps to provide robust result independent of image obscure and changing background. The necessity of median filter with this algorithm constitute it more rapid in the presence of clamor but the intended tracker finds out the human with trivial movement and precisely the moving shadow of the same person as a moving object. Performance of Optical Flow Technique  considering a strategies for a model obstruct of real and standard image consecutives. The outcomes of various routinely issues alluded to optical flow techniques enclosing a model of differential matching energy based and phase based methods are accounted. Our comparisons are basically experimental and focus on the accuracy, dependability and density of the speed measurements they demonstrate that performance can vary essentially among the methods we implemented. Segmentation of Vehicles Based on Motion Estimation that by applying an Lucas canade algorithm to evaluating the displacement vectors to identify the motions in custom to separate the vehicles from background using threshold operation and the movement of vehicles can be tracked by using blob in a video sequence.
In summary, we propose a new angle on story de- tection and tracking based on frequent pattern min- ing and real-time retrieval of tagged news articles. To the best of our knowledge there is no other method which exploits real-time hashtag recommen- dations for this purpose. We present a frequent pattern-based story detection which allows “zoom- ing in/out” into substories and superstories. The ad- vantage of our proposed story tracking solution is that it quickly adapts to emerging entities or events and their relatedness, because it does not require a slow-to-change knowledge base. Our solution is real-time and does a retrieval on-demand without the need of recomputing any clusters or semantic models when new data arrives. The weaknesses of our story tracking approach include the strong re- liance on the hashtag recommender (although Hash- tagger has 85% Precision@1) and the potential lack of story discussions on social platforms, e.g., Hash- tagger recommends at least 1 hashtag to about 65% of all articles. This can be mitigated to some ex- tent possibly by expanding our scope to other social platforms that increasingly adopt social tags. Yet an- other workaround for compensating for the partial hashtag coverage is discussed below in the future work.
At the Utwente a quite old system is used, the “Vector vision compact 4” from manufacturer Polaris, which can be seen in figure 42. Disadvantage of this system is that it is very difficult to manipulate the code from within and calibration of tools with the system is cumbersome and in various situations appeared not to be successful at all. Tool calibration with the system is necessary since the output is only the position and orientation of the tool, the internals about the position of the reflectors are not allowed to be accessed for the user. This implies also that the definition of a new tool, for example if the endoscope would be equipped with reflectors, is not easy or even not possible to be made. Tests for point registration with one of the standard probes of the system resulted in reprojection errors of more than 10mm. Due to the inconvenience of this system, and the fact that in the AvL the EM system is currently used for PDT in the sinuses, it is decided to not investigate further in the use of the optical tracking system.
IPMS (Integrated Production Management System) are computerized systems used in manufacturing. IPMS can provide the right information at the right time and show the manufacturing decision maker “how the current conditions on the plant floor can be optimized to improve production output”. IPMS work in realtime to enable the control of multiple elements of the production process.
mobile services that collect sensor data from mobile phone users, analyze these data sets to identify higher order attributes, and then feed these abstractions back to serve the users. This gives rise to sensor data driven service innovation, where mobile service ideas are generated based on patterns related to individuals as well as social networks. The key idea is to create compelling user experiences based on the patterns extracted from the sensors on the traveler’s mobile phone. Awareness of a user’s activities helps to determine the user’s carbon footprint [1, 2], or track the amount of calories burnt [3, 4]. These benefits are derived directly from the ability to automatically detect the activities of persons from their mobile phones. For another example, the transportation mode activities, such as walking, cycling, or train denotes some important characteristics of the mobile user’s context. With knowledge of a traveler’s transportation mode, targeted and customized advertisements may be sent to the traveler’s device. For example, if we discover that Alice is driving by car, the system may send her gas coupons or vehicle service specials. For another example, if we discover that Bob is traveling by bus, then his sensor reports cannot be used to predict the expected travel time on a road network. This is important for traffic and navigation providers that use travelers as probes.
The next stage is to use the neural network classifier to identify tracking errors across the entire set of patient log files. Depending on the setting of the detection threshold, the classifier becomes more or less sensitive. In figure 4 (left), we see that by setting the threshold to a false alarm rate of 5% (FAR = 0.05), a tracking error beginning at around time t = 43 s is detected accurately with relatively few false alarms, while setting the threshold to a false alarm rate of 10% (FAR = 0.10) causes significant regions to be mislabelled as errors. In contrast, consider figure 4 (right), which shows the motion trace of a patient with a small tumour motion, and sudden jumps in motion caused by loss of track. We see that at FAR = 0.05 these errors are not detected, but at FAR = 0.10 these errors are correctly identified. These examples illustrate the difficulty in setting the detection sensitivity. Therefore, in the remaining analysis we have chosen to make two estimates of tracking errors, one at FAR = 0.05 and one at FAR = 0.10, with the understanding that the true classification is probably within these bounds.
As a variety of video surveillance devices such as CCTV, drones, and car dashboard cameras have become popular, numerous studies have been conducted regarding the effective enforcement of security and surveillance based on video analysis. In particular, in car-related surveillance, car tracking is the most challenging task. One early approach to accomplish such a task was to analyze frames from different video sources separately. Considering the shooting range of the bulk of video devices, the outcome from the analysis of single video source is highly limited. To obtain more comprehensive information for car tacking, a set of video sources should be considered together and the relevant information should be integrated according to spatial and temporal constraints. Therefore, in this study, we propose a real-time car tracking system based on surveillance videos from diverse devices including CCTV, dashboard cameras, and drones. For scalability and fault tolerance, our system is built on a distributed processing framework and comprises a Frame Distributor, a Feature Extractor, and an Information Manager. The Frame Distributor is responsible for distributing the video frames from various devices to the processing nodes. The Feature Extractor extracts principal vehicle features such as plate number, location, and time from each frame. The Information Manager stores all the features into a database and handles user requests by collecting relevant information from the feature database. To illustrate the effectiveness of our proposed system, we implemented a prototype system and performed a number of experiments. We report some of the results.
The increasing availability of video sensors and high performance video processing hardware opens up exc iting potential for tackling many video surveillance proble ms. It is important to develop robust video surveillance techniques which can process large amounts of data in realtime. As an active research topic in co mputer vision, v isual surveillance in dynamic scenes attempts to detect, recognize and track certain objects from image sequences, and more generally to understand and describe object behaviors. The aim is to develop intelligent visual surveillance to replace the tradit ional passive video surveillance that is proving ineffective as the number of ca me ras exceeds the capability of human operators to monitor the m. In short, the goal of v isual surveillance is not only to put cameras in the place of hu man eyes, but also to accomplish exp ressways, detection of military targets. A survey on visual surveillance of object motion and behaviors is given in  . Real-time object tracking is the crit ical task in many co mputer vision applications such as surveillance [2, 3], augmented reality , object-based video compression , and driver assistance. In its simplest form, t racking can be defined as the problem of estimating the tra jectory of an object in the image plane as it moves around a scene. In other words, a tracke r assigns consistent labels to the tracked objects in different fra mes of a video. Additionally, depending on the tracking do ma in, a tracke r can a lso provide object -centric informat ion, such as orientation, area, o r shape of an object. In
In this paper we investigate the computational power of sensor networks in the context of a tracking application by taking a minimalist approach focused on binary sensors. The binary model assumption is that each sensor network node has sensors that can detect one bit of information and Our tracking algorithms have the flavor of particle filter-ing  and make three assumptions. First, the sensors across a region can sense the target approaching or moving away. The range of the sensors defines the size of this region which is where the active computation of the sensor network takes place (although the sensor network may extend over a larger area). The second assumption is that the bit of information from each sensor is available in a centralized repository for processing[7,8]. This assumption can be addressed by using a simple broadcast protocol in which the nodes sensing the target send their id and data bit to a base station for pro-cessing. Because the data is a single bit (rather than a complex image taken by a camera) sending this information to the base station is feasible. Our proposed approach is most practical for