Small Object Detection and Tracking: Algorithm, Analysis and Application

Desai, U.B.; Merchant, S. N.; Zaveri, Mukesh A.; Ajishna, G.; Purohit, Manoj; Phanish, H. S.

doi:10.1007/11590316_14

Cited by 18 publications

(6 citation statements)

References 12 publications

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“…In order to meet these challenges, a lot of work had already been carried out. Most of the work is in sports domain such as soccer [ 16 , 17 ], cricket [ 18 , 19 ], basketball [ 7 , 20 ], tennis [ 21 ], and ping-pong playing robots [ 22 , 23 , 24 , 25 ]. Some work is also carried out for catching robots such as the work in [ 26 ] for a ball catching robot where a ball 8.5 cm in diameter was wrapped in retro-reflective foil and its flight trajectory was observed through stereo triangulation by two cameras mounted as the eyes of a catching humanoid robot.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Tracking of Mechanically Thrown Objects by Industrial Catching Robot for Automated In-Plant Logistics 4.0

Qadeer

Shah

Sharif

et al. 2022

Sensors

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Industry 4.0 smart manufacturing systems are equipped with sensors, smart machines, and intelligent robots. The automated in-plant transportation of manufacturing parts through throwing and catching robots is an attempt to accelerate the transportation process and increase productivity by the optimized utilization of in-plant facilities. Such an approach requires intelligent tracking and prediction of the final 3D catching position of thrown objects, while observing their initial flight trajectory in real-time, by catching robot in order to grasp them accurately. Due to non-deterministic nature of such mechanically thrown objects’ flight, accurate prediction of their complete trajectory is only possible if we accurately observe initial trajectory as well as intelligently predict remaining trajectory. The thrown objects in industry can be of any shape but detecting and accurately predicting interception positions of any shape object is an extremely challenging problem that needs to be solved step by step. In this research work, we only considered spherical shape objects as their3D central position can be easily determined. Our work comprised of development of a 3D simulated environment which enabled us to throw object of any mass, diameter, or surface air friction properties in a controlled internal logistics environment. It also enabled us to throw object with any initial velocity and observe its trajectory by placing a simulated pinhole camera at any place within 3D vicinity of internal logistics. We also employed multi-view geometry among simulated cameras in order to observe trajectories more accurately. Hence, it provided us an ample opportunity of precise experimentation in order to create enormous dataset of thrown object trajectories to train an encoder-decoder bidirectional LSTM deep neural network. The trained neural network has given the best results for accurately predicting trajectory of thrown objects in real time.

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Section: Related Workmentioning

confidence: 99%

Intelligent Tracking of Mechanically Thrown Objects by Industrial Catching Robot for Automated In-Plant Logistics 4.0

Qadeer

Shah

Sharif

et al. 2022

Sensors

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show abstract

“…Ford targeted the mid‐size sedan segment in India, which was considered highly uncertain when compared to the subcompact car segment, due to the relatively less mature nature of the market, affordability of Indian consumers and other macroeconomic factors. At the time, the mid‐size sedan segment was also targeted by other multinational competitors such as GM, Suzuki, Honda, Fiat, Hyundai, and Daewoo, some of which were relatively more established players than Ford in the Indian PV market (Desai and Purohit 2003). Ford finally re‐entered the Indian PV market in 1996 with European Escort, and planned to bring the next model, Fiesta, in 1998.…”

Section: Case Studiesmentioning

confidence: 99%

To Adapt or Design: An Emerging Market Dilemma for Automakers

Jonnalagedda

Saranga

2019

Production and Operations Management

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Multinational automakers tend to be biased toward adapting products designed for their primary markets to emerging or secondary markets. The preference for such adapted product design (APD) approach arises from reduced costs associated with product design and development. However, the growing importance of emerging markets combined with the recognition of their idiosyncratic needs have led automakers to consider an alternative product development approach that simultaneously incorporates the needs of both the markets and exploits scope economies. We term this approach as the customized simultaneous design (CSD) approach. Motivated by case studies of Ford and Hyundai, we abstract key elements to set up an analytical model for the APD and CSD approaches. We derive the optimal prices and extent of commonality between products for both the markets and compare the attractiveness of the two product development approaches. The relative profitability of the two approaches is driven by the trade‐off between the consumer‐side disutilities and the cost‐side scale and scope economies, and is moderated by demand uncertainty in the secondary market. Interestingly, we find that scale and scope economies reinforce each other in making CSD a more profitable approach under certain conditions. When the automaker is located in the secondary market, other factors such as market size, uncertainty and dissimilarity between the markets determine the most profitable approach. In addition, we demonstrate that higher profitability of one approach over the other is not necessarily achieved by providing higher commonality.

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“…However, unsuitability of KF for tracking non-linearly and dynamically moving objects like ball, with changing appearance, decreased the tracking performance. As a consequence, hybrid approaches started being used which included Nearest Neighbour Data Association (NNDA) [10] and Template Matching (TM) [11]. Small and less challenging datasets resulted in their high quantitative performance.…”

Section: Introductionmentioning

confidence: 99%

A deep learning ball tracking system in soccer videos

Kamble¹,

Keskar²,

Bhurchandi³

2019

Opto-Electronics Review

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Increasing interest, enthusiasm of sport lovers, and economics involved offer high importance to sports video recording and analysis. Being crucial for decision making, ball detection and tracking in soccer has become a challenging research area. This paper presents a novel deep learning approach for 2D ball detection and tracking (DLBT) in soccer videos posing various challenges. A new 2-stage buffer median filtering background modelling is used for moving objects blob detection. A deep learning approach for classification of an image patch into three classes, i.e. ball, player, and background is initially proposed. Probabilistic bounding box overlapping technique is proposed further for robust ball track validation. Novel full and boundary grid concepts resume tracking in ball track lost and ball out of frame situations. DLBT does not require human intervention to identify ball from the initial frames unlike the most published algorithms. DLBT yields extraordinary accurate and robust tracking results compared to the other contemporary 2D trackers even in presence of various challenges including very small ball size and fast movements.

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Small Object Detection and Tracking: Algorithm, Analysis and Application

Cited by 18 publications

References 12 publications

Intelligent Tracking of Mechanically Thrown Objects by Industrial Catching Robot for Automated In-Plant Logistics 4.0

Intelligent Tracking of Mechanically Thrown Objects by Industrial Catching Robot for Automated In-Plant Logistics 4.0

To Adapt or Design: An Emerging Market Dilemma for Automakers

A deep learning ball tracking system in soccer videos

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