• Title/Summary/Keyword: Tracking-Learning-Detection

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Rotation Invariant Tracking-Learning-Detection System (회전에 강인한 실시간 TLD 추적 시스템)

  • Choi, Wonju;Sohn, Kwanghoon
    • Journal of Korea Multimedia Society
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    • v.19 no.5
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    • pp.865-873
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    • 2016
  • In recent years, Tracking-Learning-Detection(TLD) system has been widely used as a detection and tracking algorithm for vision sensors. While conventional algorithms are vulnerable to occlusion, and changes in illumination and appearances, TLD system is capable of robust tracking by conducting tracking, detection, and learning in real time. However, the detection and tracking algorithms of TLD system utilize rotation-variant features, and the margin of tracking error becomes greater when an object makes a full out-of-plane rotation. Thus, we propose a rotation-invariant TLD system(RI-TLD). we propose a simplified average orientation histogram and rotation matrix for a rotation inference algorithm. Experimental results with various tracking tests demonstrate the robustness and efficiency of the proposed system.

Robust appearance feature learning using pixel-wise discrimination for visual tracking

  • Kim, Minji;Kim, Sungchan
    • ETRI Journal
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    • v.41 no.4
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    • pp.483-493
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    • 2019
  • Considering the high dimensions of video sequences, it is often challenging to acquire a sufficient dataset to train the tracking models. From this perspective, we propose to revisit the idea of hand-crafted feature learning to avoid such a requirement from a dataset. The proposed tracking approach is composed of two phases, detection and tracking, according to how severely the appearance of a target changes. The detection phase addresses severe and rapid variations by learning a new appearance model that classifies the pixels into foreground (or target) and background. We further combine the raw pixel features of the color intensity and spatial location with convolutional feature activations for robust target representation. The tracking phase tracks a target by searching for frame regions where the best pixel-level agreement to the model learned from the detection phase is achieved. Our two-phase approach results in efficient and accurate tracking, outperforming recent methods in various challenging cases of target appearance changes.

Deep-learning Sliding Window Based Object Detection and Tracking for Generating Trigger Signal of the LPR System (LPR 시스템 트리거 신호 생성을 위한 딥러닝 슬라이딩 윈도우 방식의 객체 탐지 및 추적)

  • Kim, Jinho
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.17 no.4
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    • pp.85-94
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    • 2021
  • The LPR system's trigger sensor makes problem occasionally due to the heave weight of vehicle or the obsolescence equipment. If we replace the hardware sensor to the deep-learning based software sensor in order to generate the trigger signal, LPR system maintenance would be a lot easier. In this paper we proposed the deep-learning sliding window based object detection and tracking algorithm for the LPR system's trigger signal generation. The gate passing vehicle's license plate recognition results are combined into the normal tracking algorithm to catch the position of the vehicle on the trigger line. The experimental results show that the deep learning sliding window based trigger signal generating performance was 100% for the gate passing vehicles including the 5.5% trigger signal position errors due to the minimum bounding box location errors in the vehicle detection process.

Deep Learning-based Approach for Visitor Detection and Path Tracking to Enhance Safety in Indoor Cultural Facilities (실내 문화시설 안전을 위한 딥러닝 기반 방문객 검출 및 동선 추적에 관한 연구)

  • Wonseop Shin;Seungmin, Rho
    • Journal of Platform Technology
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    • v.11 no.4
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    • pp.3-12
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    • 2023
  • In the post-COVID era, the importance of quarantine measures is greatly emphasized, and accordingly, research related to the detection of mask wearing conditions and prevention of other infectious diseases using deep learning is being conducted. However, research on the detection and tracking of visitors to cultural facilities to prevent the spread of diseases is equally important, so research on this should be conducted. In this paper, a convolutional neural network-based object detection model is trained through transfer learning using a pre-collected dataset. The weights of the trained detection model are then applied to a multi-object tracking model to monitor visitors. The visitor detection model demonstrates results with a precision of 96.3%, recall of 85.2%, and an F1-score of 90.4%. Quantitative results of the tracking model include a MOTA (Multiple Object Tracking Accuracy) of 65.6%, IDF1 (ID F1 Score) of 68.3%, and HOTA (Higher Order Tracking Accuracy) of 57.2%. Furthermore, a qualitative comparison with other multi-object tracking models showcased superior results for the model proposed in this paper. The research of this paper can be applied to the hygiene systems within cultural facilities in the post-COVID era.

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A Study on Pedestrians Tracking using Low Altitude UAV (저고도 무인항공기를 이용한 보행자 추적에 관한 연구)

  • Seo, Chang Jin
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.67 no.4
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    • pp.227-232
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    • 2018
  • In this paper, we propose a faster object detection and tracking method using Deep Learning, UAV(unmanned aerial vehicle), Kalman filter and YOLO(You Only Look Once)v3 algorithms. The performance of the object tracking system is decided by the performance and the accuracy of object detecting and tracking algorithms. So we applied to the YOLOv3 algorithm which is the best detection algorithm now at our proposed detecting system and also used the Kalman Filter algorithm that uses a variable detection area as the tracking system. In the experiment result, we could find the proposed system is an excellent result more than a fixed area detection system.

An Efficient Vision-based Object Detection and Tracking using Online Learning

  • Kim, Byung-Gyu;Hong, Gwang-Soo;Kim, Ji-Hae;Choi, Young-Ju
    • Journal of Multimedia Information System
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    • v.4 no.4
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    • pp.285-288
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    • 2017
  • In this paper, we propose a vision-based object detection and tracking system using online learning. The proposed system adopts a feature point-based method for tracking a series of inter-frame movement of a newly detected object, to estimate rapidly and toughness. At the same time, it trains the detector for the object being tracked online. Temporarily using the result of the failure detector to the object, it initializes the tracker back tracks to enable the robust tracking. In particular, it reduced the processing time by improving the method of updating the appearance models of the objects to increase the tracking performance of the system. Using a data set obtained in a variety of settings, we evaluate the performance of the proposed system in terms of processing time.

A Self-Supervised Detector Scheduler for Efficient Tracking-by-Detection Mechanism

  • Park, Dae-Hyeon;Lee, Seong-Ho;Bae, Seung-Hwan
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.10
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    • pp.19-28
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    • 2022
  • In this paper, we propose the Detector Scheduler which determines the best tracking-by-detection (TBD) mechanism to perform real-time high-accurate multi-object tracking (MOT). The Detector Scheduler determines whether to run a detector by measuring the dissimilarity of features between different frames. Furthermore, we propose a self-supervision method to learn the Detector Scheduler with tracking results since it is difficult to generate ground truth (GT) for learning the Detector Scheduler. Our proposed self-supervision method generates pseudo labels on whether to run a detector when the dissimilarity of the object cardinality or appearance between frames increases. To this end, we propose the Detector Scheduling Loss to learn the Detector Scheduler. As a result, our proposed method achieves real-time high-accurate multi-object tracking by boosting the overall tracking speed while keeping the tracking accuracy at most.

A New CSR-DCF Tracking Algorithm based on Faster RCNN Detection Model and CSRT Tracker for Drone Data

  • Farhodov, Xurshid;Kwon, Oh-Heum;Moon, Kwang-Seok;Kwon, Oh-Jun;Lee, Suk-Hwan;Kwon, Ki-Ryong
    • Journal of Korea Multimedia Society
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    • v.22 no.12
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    • pp.1415-1429
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    • 2019
  • Nowadays object tracking process becoming one of the most challenging task in Computer Vision filed. A CSR-DCF (channel spatial reliability-discriminative correlation filter) tracking algorithm have been proposed on recent tracking benchmark that could achieve stat-of-the-art performance where channel spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process with only two simple standard features, HoGs and Color names. However, there are some cases where this method cannot track properly, like overlapping, occlusions, motion blur, changing appearance, environmental variations and so on. To overcome that kind of complications a new modified version of CSR-DCF algorithm has been proposed by integrating deep learning based object detection and CSRT tracker which implemented in OpenCV library. As an object detection model, according to the comparable result of object detection methods and by reason of high efficiency and celerity of Faster RCNN (Region-based Convolutional Neural Network) has been used, and combined with CSRT tracker, which demonstrated outstanding real-time detection and tracking performance. The results indicate that the trained object detection model integration with tracking algorithm gives better outcomes rather than using tracking algorithm or filter itself.

Simple Online Multiple Human Tracking based on LK Feature Tracker and Detection for Embedded Surveillance

  • Vu, Quang Dao;Nguyen, Thanh Binh;Chung, Sun-Tae
    • Journal of Korea Multimedia Society
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    • v.20 no.6
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    • pp.893-910
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    • 2017
  • In this paper, we propose a simple online multiple object (human) tracking method, LKDeep (Lucas-Kanade feature and Detection based Simple Online Multiple Object Tracker), which can run in fast online enough on CPU core only with acceptable tracking performance for embedded surveillance purpose. The proposed LKDeep is a pragmatic hybrid approach which tracks multiple objects (humans) mainly based on LK features but is compensated by detection on periodic times or on necessity times. Compared to other state-of-the-art multiple object tracking methods based on 'Tracking-By-Detection (TBD)' approach, the proposed LKDeep is faster since it does not have to detect object on every frame and it utilizes simple association rule, but it shows a good object tracking performance. Through experiments in comparison with other multiple object tracking (MOT) methods using the public DPM detector among online state-of-the-art MOT methods reported in MOT challenge [1], it is shown that the proposed simple online MOT method, LKDeep runs faster but with good tracking performance for surveillance purpose. It is further observed through single object tracking (SOT) visual tracker benchmark experiment [2] that LKDeep with an optimized deep learning detector can run in online fast with comparable tracking performance to other state-of-the-art SOT methods.

Implementation and Verification of Deep Learning-based Automatic Object Tracking and Handy Motion Control Drone System (심층학습 기반의 자동 객체 추적 및 핸디 모션 제어 드론 시스템 구현 및 검증)

  • Kim, Youngsoo;Lee, Junbeom;Lee, Chanyoung;Jeon, Hyeri;Kim, Seungpil
    • IEMEK Journal of Embedded Systems and Applications
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    • v.16 no.5
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    • pp.163-169
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    • 2021
  • In this paper, we implemented a deep learning-based automatic object tracking and handy motion control drone system and analyzed the performance of the proposed system. The drone system automatically detects and tracks targets by analyzing images obtained from the drone's camera using deep learning algorithms, consisting of the YOLO, the MobileNet, and the deepSORT. Such deep learning-based detection and tracking algorithms have both higher target detection accuracy and processing speed than the conventional color-based algorithm, the CAMShift. In addition, in order to facilitate the drone control by hand from the ground control station, we classified handy motions and generated flight control commands through motion recognition using the YOLO algorithm. It was confirmed that such a deep learning-based target tracking and drone handy motion control system stably track the target and can easily control the drone.