Wangsiripitak, Somkiat
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Wangsiripitak, Somkiat
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somkiat.wa@kmitl.ac.th
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Item type:Publication, Pixel-based foreground detection in repetitive time-series region(2019-01-01); Currently, many state-of-the-art background subtraction techniques cannot deal properly with the area of periodic changing background, while some continue classifying them as foreground at intervals, others simply mask that area as a non-region of interest. To cope with this issue, a novel method of detecting repetitive temporal patterns based on the image sequences was proposed in this paper. The main emphasis of the proposed approach is on classifying those pixels as a background and identifying foreground objects in their relevant areas. As for the foreground detection, a model of time series pattern found in each pixel is individually built first; and then, any changes beyond the allowance of model periodicity are then determined as foreground objects. The proposed method could be used and run in parallel with any state-of-the-art background subtraction technique, allowing more accurate foreground-background segmentation. Experimental results showed that using Y channel, the proposed method of detecting time-series background area could achieve 92.9% of recall rate with less than 1% false positives. The recall of foreground detection in an area of repetitive time-series pattern was about 87%; while F-measure was about 0.73 on average. The false positives of foreground detection were also less than 1%. Accordingly, the proposed time-delay detection technique could significantly help to suppress the foreground error on time series background area, especially during the change from one sub-pattern to another which causes a camera sensor to capture both sub-pattern values in one frame. Performance comparison with state-of-the-art methods showed that our proposed method was able to reduce 80% of the average false alarm and improve F-measure to 28% while the computational efficiency was reduced by only 1%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Feature-based motion detection and tracking on approximate 3D ground plane(2017-01-04); Pluempitiwiriyawej, CharnchaiThe success of movement detection based on the distance moved in a 2D image sequence depends highly on the angle between a camera's optical axis and the normal vector of the ground plane on which the moving object is traveling. When the same 3D displacement occurs at various positions in the scene, the higher the angle is, the greater the distance observed in the image at a position close to camera differs from (strictly speaking, is larger than) those happening at the far end. As a consequence, a detection failure and/or false alarm may occur if no such 3D geometry is utilized. This paper estimates a 3D ground plane, which is then used to measure the approximate 3D displacement of features being detected and tracked. The 3D distances are therefore available and utilized in deciding whether they are of moving objects or just blinking features caused by illumination changes. FAST points are used to enhance a real-time system. Experimental results show superior performance in tracking: a longer trace of continuous tracking, a higher number of detected moving features, earlier detection, better recall rate, no misses, and no false alarms. A SURF descriptor and FLANN matcher were utilized here, however the robustness was not much enhanced when compared to the expense of finding the best match. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, In-building navigation system using a camera(2014-01-01)A top-view map of building floor is still used nowadays by a visitor for in-building navigation. Its practicality heavily relies on the user's skill in matching a current position on the map. When the map is installed at a fixed position, not held in hand, the position of the map itself can be marked in the map, allowing the user to know the current position. Searching for the map itself, however, is still a necessary task. An orientation of the user is another issue of this traditional approach. It can be solved by having one more map point matched with the corresponding physical position on the floor. People with a good sense of direction have no difficulties with this conventional in-building navigation, but neither do the others. This paper uses the PTAM (parallel tracking and mapping - one type of visual simultaneous localization and mapping, SLAM) as a base system in which a camera is the only sensor. Two map points and their corresponding real position are used for registration of the camera pose acquired from PTAM with the top-view map coordinates. The top-view map of current floor is the only thing obtained in advance. Experimental results show that the proposed monocular navigation system can be used to navigate in the building in real-time, which helps the user correctly decide the route to the destination, similar to a GPS system used for the vehicle navigation. © 2013 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Tracking-based human entry/exit detection on various video resolutions (A study on parameter effects)(2015-01-01); A real-time tracking-based change detection using FAST features and a background feature model are proposed as a base system for detection of human entrance and exit. A speedy FAST feature extraction and tracking has to trade-off its accuracy, which sometimes causes a failure in human entrance/exit detection. Many video sizes are therefore tested in the system to examine the trade-off effects on the accuracy of feature extraction, tracking, and entry/exit detection. Tracking parameters are also investigated to determine the optimal values for each video resolution, such that stable tracking and detection are achieved. Experimental results show that the higher the video resolution is the more the error is likely to happen. Instability of feature extraction and position which increases in higher resolution is proved to be the main reason of failure. Increasing the number of previous images used in the update of background feature model, proportional to the resolution of video, takes into account the feature uncertainty. As the result, the proposed method is robust to changes in video resolution and runs at 30 fps without a miss of human entrance/exit detection and false alarm. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Real-time monocular human height estimation using bimodal background subtraction(2017-12-19); Saelao, WongsatornA human height is one property used in conjunction with others for person identification. The method of human height estimation, using only one camera and some simple settings on the floor, is proposed to automatically determine the vertical distance of human head from the ground in real-time. Bimodal background subtraction technique helps locate the position of head top and lower foot bottom in each image frame, and the upright distance from the lower foot to the position closest to the head top is defined as the candidate of human height in the corresponding frame. A distribution of all heights estimated so far is finally used to determine the height of human in that frame. The proposed method establishes the range of possible heights from all frames in real-time - this information potentially helps increase the performance of automatic person identification in surveillance system. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Real-Time Vision Based Human Height Measurement Using Sliding Window on Selected Candidates(2018-07-02) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Sketch image classification using component based k-Nn(2019-02-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Vision-based system for automatic detection of suspicious objects on ATM(2015-01-01); Most skimming devices attached to an automatic teller machine (ATM) are similar in color and shape to the host machine, vision-based detection of such things is therefore difficult. A background subtraction method may be used to detect changes in a normal situation. However, without human detection, its background model is sometimes polluted by the ATM user, and the method cannot detect suspicious objects left in the scene. This paper proposes a real-time system which integrates (i) a simple image subtraction for detection of user arrival and departure, and (ii) an automatic detection of suspicious objects left on the ATM. The background model is updated only when no user is found, and used to detect suspicious objects based on a guided adaptive threshold. To avoid a detection miss, nonlinear enhancement is applied to amplify the intensity differences between foreign objects and host machine. Experimental results show that the proposed system increases correctly detected area by 13.21% compared with the fixed threshold method. It has no detection miss and false alarm either. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, CDoTS: Change detection on time series background for video foreground segmentation(2017-11-03); Although many adaptive background subtraction methods have been proposed for image-based foreground detection, dynamic background in the scene, such as an electronic billboard, still causes a serious problem of false alarm. Exclusion of such area from region of interest may prevent the problem, however an issue of security hole on that area becomes another concern. A method of change detection on repetitive time series background is proposed in this paper. Our method extends an adaptive multiresolution background subtraction to allow detection of time series, which is in turn used for foreground extraction on such area. The accuracy of segmentation on static background is barely changed, while that on the area of periodic change is significantly improved. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparison of gesture in Thai boxing framework using angular dynamic time warping(2019-07-01)