On-Line Visual Tracking with Occlusion Handling

Rathnayake, Tharindu; Gostar, Amirali Khodadadian; Hoseinnezhad, Reza; Tennakoon, Ruwan; Bab–Hadiashar, Alireza

doi:10.3390/s20030929

Cited by 19 publications

(3 citation statements)

References 63 publications

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“…However, methods utilizing vision or lasers come with the drawback of requiring large computational resources [27,28]. Additionally, vision and laser sensing faces challenges when temporary occlusions occur, such as when an obstacle momentarily obscures the person, making it difficult to discern their posture [29,30].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Human Movement Recognition Using Ultra-Wideband Sensors

Noh,

Ahn,

Lee

2024

Electronics

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This study introduces a methodology for the real-time detection of human movement based on two legs using ultra-wideband (UWB) sensors. Movements were primarily categorized into four states: stopped, walking, lingering, and the transition between sitting and standing. To classify these movements, UWB sensors were used to measure the distance between the designated point and a specific point on the two legs in the human body. By analyzing the measured distance values, a movement state classification model was constructed. In comparison to conventional vision/laser/LiDAR-based research, this approach requires fewer computational resources and provides distinguished real-time human movement detection within a CPU environment. Consequently, this research presents a novel strategy to effectively recognize human movements during human–robot interactions. The proposed model effectively discerned four distinct movement states with classification accuracy of around 95%, demonstrating the novel strategy’s efficacy.

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Section: Introductionmentioning

confidence: 99%

Real-Time Human Movement Recognition Using Ultra-Wideband Sensors

Noh,

Ahn,

Lee

2024

Electronics

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“…According to [ 13 ], there are two methods of estimating the target’s trajectory, online and batch. The online method uses the current and previous frames to estimate the state of the target in each time period and the batch method uses the entire sequence of frames to optimize the target estimate in each time using past and future information, however, the batch method cannot be used in applications where it is necessary to track the target in real time.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking

Castro

Salles

Ciarelli

2021

Sensors

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This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking targets in environments with almost constant lighting. This approach also delimits the solution space for a more efficient search. A robust version to outliers of the double exponential smoothing (DES) model is used to predict the target position in the frame delimiting the solution space in a more promising region for target tracking. To assess the quality of the proposed approach, an appropriate tracker for a discrete solution space was implemented using the meta-heuristic Shuffled Frog Leaping Algorithm (SFLA) adapted to dynamic optimization problems, named the Dynamic Shuffled Frog Leaping Algorithm (DSFLA). The DSFLA was compared with other classic and current trackers whose algorithms are based on swarm intelligence. The trackers were compared in terms of the average processing time per frame and the area under curve of the success rate per Pascal metric. For the experiment, we used a random sample of videos obtained from the public Hanyang visual tracker benchmark. The experimental results suggest that the DSFLA has an efficient processing time and higher quality of tracking compared with the other competing trackers analyzed in this work. The success rate of the DSFLA tracker is about 7.2 to 76.6% higher on average when comparing the success rate of its competitors. The average processing time per frame is about at least 10% faster than competing trackers, except one that was about 26% faster than the DSFLA tracker. The results also show that the predictions of the robust DES model are quite accurate.

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“…• Occlusion: Occlusion is a primary challenge in performing long-term occupancy analysis and many other vision analysis applications. In Many methods [124][125][126][127][128][129] have been proposed to meet this challenge.…”

Section: General Challangesmentioning

confidence: 99%

Occupancy Analysis of the Outdoor Football Fields

Huda

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On-Line Visual Tracking with Occlusion Handling

Cited by 19 publications

References 63 publications

Real-Time Human Movement Recognition Using Ultra-Wideband Sensors

Real-Time Human Movement Recognition Using Ultra-Wideband Sensors

A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking

Occupancy Analysis of the Outdoor Football Fields

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