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How to find a person by video

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Nein, nur der Nutzer, der das Foto oder Video geteilt hat, kann Personen darin markieren. Hinweis: Wenn du ein Foto oder Video in deinem Profil verbirgst, wirst du nicht daraus entfernt. So kannst du dich aus einem Foto oder Video entfernen, in dem dich jemand markiert hat. Im Profil anzeigen Android aus. Abgesehen von den Nutzern, die du blockiert hast, kann dich jeder in seinen Fotos und Videos auf Instagram markieren. Du kannst Personen in einem Foto oder Video markieren, das du auf Instagram teilst.

SEE VIDEO BY TOPIC: How To Find Someone 's Location By Their Phone Numbr On Your Android Device!

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Person identification plays an important role in semantic analysis of video content. This paper presents a novel method to automatically label persons in video sequence captured from fixed camera. Instead of leveraging traditional face recognition approaches, we deal with the task of person identification by fusing information from motion sensor platforms, like smart phones, carried on human bodies and extracted from camera video.

More specifically, a sequence of motion features extracted from camera video are compared with each of those collected from accelerometers of smart phones. When strong correlation is detected, identity information transmitted from the corresponding smart phone is used to identify the phone wearer. To test the feasibility and efficiency of the proposed method, extensive experiments are conducted which achieved impressive performance. With the rapid growth in storage devices, networks, and compression techniques, large-scale video data have become available to more and more ordinary users.

Thus, it also becomes a challenging task to search and browse desirable data according to content in large video datasets. Generally, person information is one of the most important semantic clues when people are recalling video contents. Consequently, person identification is crucial for content based video summary and retrieval. The main purpose of person identification is to associate each subject that appears in video clips with a real person.

However, to manually label all subjects that appear in a large-scale video archive is labor intensive, time consuming, and prohibitively expensive. To deal with this, automatic face detection [ 1 — 3 ] and face recognition FR [ 4 — 7 ] were introduced.

However, traditional FR methods are still far from supporting practical and reliable automatic person identification, even when just a limited number of people appear in the video. This is mainly due to the fact that only appearance information e. Specifically, variation in illumination, pose, and face expression as well as partial or total face occlusion could all make recognition an extremely difficult task.

The main contributions of the proposed method are as follows. First, this method provides an alternative way towards automatic person identification by the integration of a new sensing model. This integration broadens the domain of semantic analysis of video content and will be catalyzed by the growing popularity of wearable devices and concurrent advance in personal sensing technology and ubiquitous computing. Second, the method is fully automatic without any need for establishing a predefined model or need for user interaction in the process of person identification.

Moreover, the independence of any recognition technique makes the proposed method more robust with respect to issues mentioned above which degrade the efficiency and accuracy of FR techniques. Last but not least, the simplicity and computational efficiency of the method make it possible to plug into real-time systems.

To improve the performance of person identification, contextual information was utilized in recent research. Authors in [ 8 ] proposed a framework exploiting heterogeneous contextual information including clothing, activity, human attributes, gait, and people cooccurrence, together with facial features to recognize a person in low quality video data. Nevertheless, it suffers the difficulty in discerning multiple persons resembling each other in clothing color or action.

View angle and subject-to-camera distance were integrated to identify person in video by fusion of gait and face in [ 9 ], only in situations when people walk along a straight path with five quantized angles. Temporal, spatial, and social context information was also employed in conjunction with low level feature analysis to annotate person in personal and family photo collections [ 10 — 14 ], in which only static images are dealt with. Moreover, in all these methods, a predefined model has to be trained to start the identification process and the performance is limited by the quality and scale of training sets.

In contrast to the above efforts we propose a novel method to automatically identify person in video using human motion pattern. We argue that, in the field of view FOV of a fixed camera, motion pattern of human body is unique. Under this assumption, except for visual analysis, we also analyze the motion pattern of human body measured by sensor modules in smart phone. In this paper, we use smart phones equipped with 3-axis accelerometers carried on human bodies to collect and transmit acceleration information and identity information.

By analyzing the correlation between motion features extracted from two different types of sensing, the problem of person identification is properly handled simply and accurately.

The remainder of the paper is organized as follows. Section 3 details the proposed method. In Section 4 , experiments are conducted and results are discussed. Concluding remarks are placed in Section 5. A flowchart of the proposed method is depicted in Figure 1.

As can be seen, visual features of human body are first extracted to track people across different video frames. Then, optical flows of potential human body are estimated and segmented using the previously obtained body features. Meanwhile, accelerometer measurements from smart phones on human bodies are transmitted and collected, together with identity information. Motion features are calculated from both optical flow and acceleration measurements in a sliding window style which was depicted in Section 3.

When people disappear from video sequences, correlation analysis starts the annotation process. Details of the method are illustrated in the following subsections. First of all, background subtraction BGS which is widely adopted in moving objects detection in video is utilized in our method. In this subsection, we need to detect image patches corresponding to potential human bodies moving around in the camera FOV.

To this end, an algorithm of adaptive Gaussian mixture model [ 16 , 17 ] is employed to segment foreground patches. This algorithm represents each pixel by a mixture of Gaussians to build a robust background model in run time. When people enter into the camera FOV, image patches corresponding to potential human bodies are extracted and tracked by descriptors composed of patch ID, color histograms, and patch mass center in Algorithm 1. Moreover, we also include the frame index of first and last appearance of each patch in the descriptor in order to facilitate person annotation.

For patch obtained from BGS, we try to associate to previous patch descriptors. Histogram similarity between patches from consecutive frames is first analyzed. Normally image patches corresponding to the same subject are more similar to each other than those of different subjects. The comparison of color histogram of paths used in Algorithm 1 is defined in 1.

The range of is. The larger , the more similar patches and. Then from the set of similar descriptors of , the nearest one is selected to track in terms of horizontal movement of patch center: where is number of bins in histogram. For each patch , we employ optical flow method to estimate motion pattern [ 18 ] and approximate patch acceleration as mean of vertical acceleration of keypoints within it, as defined in where is the second order derivative of coordinate of keypoints with respect to time.

Pseudocode of patch tracking and motion estimation is listed in Algorithm 1. In this subsection we depict the procedure of acceleration measurements collection using wearable sensors. Android smart phones equipped with 3-axis accelerometers are utilized as sensing platforms. For the three component accelerometer readings, only the one with largest absolute mean value is analyzed in our experiments due to its best reflection of vertical motion pattern of human body.

Three different placements are tested and compared in order to assess impacts of different phone placements on accuracy of motion collection. In each test, a participant performs a set of activities randomly including standing, walking, and jumping while carrying three smart phones on body, with two phones placed in chest pocket and jacket side pocket, respectively, and one attached to waist belt, as shown in Figure 2.

Results illustrated in Figure 3 qualitatively show that all three types of placement could correctly capture vertical motion feature of the participant with minor acceptable discrepancy. This test makes the choice of phone attachment more flexible and unobtrusive. Noisy raw motion measurements of different sample frequency previously obtained from different sensor sources cannot be compared directly.

Instead, standard deviation and energy [ 19 , 20 ] are employed as motion features for comparison after noise suppression and data cleansing. Energy is defined as sum of squared discrete FFT component magnitudes of data samples and divided by sample count for normalization. These features are computed in a sliding window of length with overlapping between consecutive windows. Feature extraction on sliding windows with 50 percent overlapping has demonstrated its success in [ 21 ]: To find out whether represents a human body, correlation analysis is conducted.

As a matter of fact, motion features extracted from video frames are supposed to be positively linear with those from accelerometer measurements of the same subject.

We adopt correlation coefficient to reliably measure strength of linear relationship, as defined in 4 , where and are motion features to be compared, the covariance, and and the standard deviation of and. The larger , the more correlated and. In our case, motion features of are compared with each of those extracted from smart phones in the same period of time. Identity information of smart phone corresponding to the largest positive correlation coefficient is utilized to identify.

In this section, we conduct detailed experiments in various situations to optimize Algorithm 1 and evaluate the proposed person identification algorithm. We use a digital camera and two Android smart phones for data collection.

A simple GUI application is created to start and stop data collection on phones. Acceleration measurements are recorded and saved in text files on phone SD card and later accessed via USB. Video clips are recorded in the format of mp4 files at a resolution of , 15 frames per second. The timestamps of video frames and accelerometer readings are well synchronized before the experiment. Algorithm 1 is implemented based on OpenCV library and tested on an Intel 3.

We recruit two participants, labeled as A and B, respectively, to take part in our experiments and place smart phones in jacket side pockets. We choose four different scenarios to perform our experiments, including outdoor near field, outdoor far field, indoor near field, indoor far field , as illustrated in Figure 8.

In near field situations, the subjects moved around within a scope about five meters away from the camera. The silhouette height of human body is not less than half of the image height and human face could be clearly distinguished.

In far field situations, the subjects moved around about twenty meters away where detailed visual features of human body are mostly lost and body height in image is not more than thirty pixels. In each scenario, we repeated the experiment four times and each lasts about five minutes. In all we collect sixteen video clips and thirty-two text files of acceleration measurements.

Patch tracking is an essential step for motion estimation from camera video and directly affects accuracy and robustness of subsequent person identification.

As listed in Algorithm 1 , the aim of patch tracking is to estimate motion measurements for each patch that appeared in video frames. In the ideal case, a subject is continuously tracked in camera video by only one descriptor during the whole experiment and we could extract a sequence of acceleration measurements closest to that collected from the smart phone in terms of time duration, while in the worst case, we have to create new descriptors for all patches in each frame and the number of descriptors used for tacking a subject is as many as that of the frames of his appearance.

We present a metric in 5 to measure the performance of Algorithm 1. The metric is defined as a ratio between number of subjects in a video clip and number of descriptors used for tracking the subjects. The larger , the better the tracking performance.

Moreover, we also provide a metric to evaluate tracking accuracy, as shown in 6. Accurate descriptor means that a descriptor tracks only one subject during its lifetime. The larger , the more accurate Algorithm 1 :.

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Person identification plays an important role in semantic analysis of video content. This paper presents a novel method to automatically label persons in video sequence captured from fixed camera. Instead of leveraging traditional face recognition approaches, we deal with the task of person identification by fusing information from motion sensor platforms, like smart phones, carried on human bodies and extracted from camera video. More specifically, a sequence of motion features extracted from camera video are compared with each of those collected from accelerometers of smart phones. When strong correlation is detected, identity information transmitted from the corresponding smart phone is used to identify the phone wearer. To test the feasibility and efficiency of the proposed method, extensive experiments are conducted which achieved impressive performance. With the rapid growth in storage devices, networks, and compression techniques, large-scale video data have become available to more and more ordinary users. Thus, it also becomes a challenging task to search and browse desirable data according to content in large video datasets. Generally, person information is one of the most important semantic clues when people are recalling video contents.

How to Locate a Person on YouTube

Christopher Greenwell, PhD , is a professor in the department of health and sport science at the University of Louisville in Kentucky. He has taught event management since and published several articles on unique aspects of the service environment at sporting events and how these can be used as an effective marketing tool. Greenwell has direct experience as an event manager, having planned and coordinated the event management, promotions, and game operations for all athletic events in an NCAA Division I athletic program. Events under his management set attendance records in men's and women's basketball, volleyball, and women's soccer.

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I think everyone should have decent online stalking skills. Not because I condone stalking, but because knowledge is power -- if you don't know how to find people online, how do you know what people can find about you online? Googling yourself is like checking your credit report for inaccuracies: it's only effective as a preventative measure if you do it thoroughly and routinely.

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Automatic Person Identification in Camera Video by Motion Correlation

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