Wangsiripitak, Somkiat

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School of Information Technology

somkiat.wa@kmitl.ac.th

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Pixel-based foreground detection in repetitive time-series region
(2019-01-01)
Rattanapitak, Wirat
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Wangsiripitak, Somkiat
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%.
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Feature-based motion detection and tracking on approximate 3D ground plane
(2017-01-04)
Saelao, Wongsatorn
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Wangsiripitak, Somkiat
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Pluempitiwiriyawej, Charnchai
The 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.
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In-building navigation system using a camera
(2014-01-01)
Wangsiripitak, Somkiat
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.
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Visual programming using flowchart
(2006-12-01)
Charntaweekhun, Kanis
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Wangsiripitak, Somkiat
Nowadays a computer programming become the fundamental knowledge for people studying or working in a computer field, e.g. computer engineering, computer science, information technology, etc. Programmer not only considers the algorithm carefully but also performs the coding according to the syntax of programming language used. Therefore writing a computer program is a difficult task, especially for a beginner. In fact, a novice often uses a flowchart as a tool to help him summarize the idea into all necessary steps of solution, but this flowchart cannot be understood by the computer. It must be converted to the program by using some programming language and then be compiled and run. The visual programming using flowchart proposed in this paper allows the programmer to write the program in the format of flowchart, then compiles and run, without the coding step. There is no necessity to remember the syntax of any programming languages. It takes the benefits of easy-to-understand and easy-to-perform of the flowchart, whereas eliminates the weakness of a conventional programming, e.g. the requirement of remembering the syntax, the error in coding step. Furthermore, a debugging of program by the proposed system is straightforward and easy to discover the error. The proposed system could be used as a tool for teaching the basic concept of structural programming as well. Experimental results show the powerfulness, easiness, and userfriendliness of the proposed system. © 2006 IEEE.
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On-line lao handwritten recognition with proportional invariant feature
(2005-12-01)
Bounnady, Khampheth
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Kruatrachue, Boontee
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Wangsiripitak, Somkiat
This paper proposed high level feature for online Lao handwritten recognition. This feature must be high level enough so that the feature is not change when characters are written by different persons at different speed and different proportion (shorter or longer stroke, head, tail, loop, curve). In this high level feature, a character is divided in to sequence of curve segments where a segment start where curve reverse rotation (counter clockwise and clockwise). In each segment, following features are gathered cumulative change in direction of curve (- for clockwise), cumulative curve length, cumulative length of left to right, right to left, top to bottom and bottom to top (cumulative change in X and Y axis of segment). This feature is simple yet robust for high accuracy recognition. The feature can be gather from parsing the original time sampling sequence X, Y point of the pen location without re-sampling. We also experiment on other segmentation point such as the maximum curvature point which was widely used by other researcher. Experiments results show that the recognition rates are at 94.62% in comparing to using maximum curvature point 75.07%. This is due to a lot of variations of turning points in handwritten. COPYRIGHT © ENFORMATIKA.
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Tracking-based human entry/exit detection on various video resolutions (A study on parameter effects)
(2015-01-01)
Saelao, Wongsatorn
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Rattanapitak, Wirat
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Wangsiripitak, Somkiat
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.
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Vision-Based Human Movement Matching for Muay Thai Training Support System
(2024-01-01)
Wangsiripitak, Somkiat
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Pipatbowornkul, Napong
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Wedsuwan, Nawin
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Wattanacheep, Bhattarabhorn
An algorithm is proposed to evaluate Muay Thai trainees based on body movement matching with the trainer, aiming to support Muay Thai training. Skeletons of the trainee in image sequences captured by a monocular camera are extracted by a pre-trained deep-learning model. They are then synchronized with those of the trainer by matching keyframes and subsequently other image frames between both sequences using dynamic time warping. In each matched frame, the skeleton lines (bones) are compared by measuring the angle deviation of each bone from the trainee to the corresponding bone of the trainer. The similarity score of each bone pair is computed based on a cosine similarity metric. Simultaneously, a predefined upper-value limit of the maximum expected difference in bone comparison is applied to enhance the difference in similarity scores between excellent and underperformed postures, making the evaluation results more distinctive. Each matched frame is then scored by weighted averaging of bone scores, allowing the evaluation to be adaptively focused on significant parts of the body according to movement types. The overall score of the sequence is an average over all frames. Experimental results demonstrate that the proposed scoring method is flexible for use with an off-the-shelf camera and adaptive to a marking scheme that often changes with training strategies and patterns. Prior matching of keyframes reduces the mismatching of similar but non-corresponding frames. The score is also normalized to penalize misoriented bones, resulting in even lower scores for trainees with less skill.
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Real-time monocular human height estimation using bimodal background subtraction
(2017-12-19)
Wangsiripitak, Somkiat
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Saelao, Wongsatorn
A 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.
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Real-Time Vision Based Human Height Measurement Using Sliding Window on Selected Candidates
(2018-07-02)
Dokthurian, Siriporn
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Pluempitiwiriyawej, Charnchai
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Wangsiripitak, Somkiat
This paper presents a real-time human height estimation using image sequences obtained from a single calibrated camera. For each image frame, the candidate value of human height is calculated based on the approximated 3D ground plane and 2D positions of human head top and foot bottom. Some candidates whose values do not differ much from the previous height estimate are selected; a sliding window is then applied on an array of those candidate height values; candidate heights bounded inside the sliding window that has the maximum votes and some candidate heights around that window are finally used in final height estimate. The proposed algorithm of sliding window on selected candidate helps achieve the average accuracy of height estimate at 99.05%; it is a 1.30% increase of accuracy when compared to the final height estimate from all candidates. The standard deviation also decreases from 4.26 to 1.58; the proposed method is superior in terms of accuracy and stability.
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Multimodal Biometrics Recognition Using a Deep Convolutional Neural Network with Transfer Learning in Surveillance Videos
(2022-07-01)
Aung, Hsu Mon Lei
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Pluempitiwiriyawej, Charnchai
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Hamamoto, Kazuhiko
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Wangsiripitak, Somkiat
Biometric recognition is a critical task in security control systems. Although the face has long been widely accepted as a practical biometric for human recognition, it can be easily stolen and imitated. Moreover, in video surveillance, it is a challenge to obtain reliable facial information from an image taken at a long distance with a low-resolution camera. Gait, on the other hand, has been recently used for human recognition because gait is not easy to replicate, and reliable information can be obtained from a low-resolution camera at a long distance. However, the gait biometric alone still has constraints due to its intrinsic factors. In this paper, we propose a multimodal biometrics system by combining information from both the face and gait. Our proposed system uses a deep convolutional neural network with transfer learning. Our proposed network model learns discriminative spatiotemporal features from gait and facial features from face images. The two extracted features are fused into a common feature space at the feature level. This study conducted experiments on the publicly available CASIA-B gait and Extended Yale-B databases and a dataset of walking videos of 25 users. The proposed model achieves a 97.3 percent classification accuracy with an F1 score of 0.97and an equal error rate (EER) of 0.004.

Wangsiripitak, Somkiat

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