Object tracking on event cameras with offline–online learning

Jiang, Rui; Mou, Xiaozheng; Shi, Shunshun; Zhou, Yufen; Wang, Qinyi; Dong, Meng; Chen, Shoushun

doi:10.1049/trit.2019.0107

Cited by 54 publications

(31 citation statements)

References 33 publications

(42 reference statements)

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“…As shown in the right side of Figure 4, we build the inter-frame tracker based on Ref. [28] to further enhance the tracking performance on fast-moving objects. The events between two frames are collected for object-level velocity estimation.…”

Section: Flow-assisted Inter-frame Trackingmentioning

confidence: 99%

“…A frame-based object detection module in Ref. [28] is required in this work to initialize the tracker and to improve tracking stability. On one hand, events between frames are assigned to objects, whose bounding boxes are from the current estimated ones.…”

Section: Overview Of the Proposed Approachesmentioning

confidence: 99%

“…| FLOW-ASSISTED INTER-FRAME TRACKINGAs shown in the right side of Figure4, we build the inter-frame tracker based on Ref [28]. to further enhance the tracking performance on fast-moving objects.…”

mentioning

confidence: 99%

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Flow‐assisted visual tracking using event cameras

Jiang¹,

Wang

Shi³

et al. 2021

CAAI Trans on Intel Tech

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The data from event cameras not only portray contours of moving objects but also contain motion information inherently. Herein, motion information can be used in eventbased and frame-based object trackers to ease the challenges of occluded objects and data association, respectively. In the event-based tracker, events within a short interval are accumulated. Within the interval, the histogram of local time measurements (or 'motion histogram') is proposed as the feature to describe the target and candidate regions. Then the mean-shift tracking approach is used by shifting the tracker towards similarity maximisation on motion histograms between target and candidate regions. As for the frame-based tracker, given the assumption that a single object moves at a constant velocity on the image plane, the distribution of local timestamps is modelled, followed by which object-level velocities are obtained from parameter estimation. We then build a Kalman-based ensemble, in which object-level velocities are deemed as an additional measurement on top of object detection results. Experiments have been conducted to measure the performance of proposed trackers based on our self-collected data. Thanks to the assistance from motion information, the event-based tracker successfully differentiates partially overlapped objects with distinct motion profiles; The inter-frame tracker avoids data association failure on fast-moving objects and leads to fast convergence on object velocity estimation. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Flow-assisted Inter-frame Trackingmentioning

confidence: 99%

Section: Overview Of the Proposed Approachesmentioning

confidence: 99%

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Flow‐assisted visual tracking using event cameras

Jiang¹,

Wang

Shi³

et al. 2021

CAAI Trans on Intel Tech

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“…The community detection algorithms (CDAs) have been designed to unveil some essential insights, such as human interactions in social networks, 9,10 subject descriptions in information networks, 11,12 interaction between neurons, 13 the association among terrorists in the criminal network, 14,15 and so forth. Community identification tools raise multiple considerable privacy concerns.…”

Section: Introductionmentioning

confidence: 99%

Intelligent deception techniques against adversarial attack on the industrial system

Kumari¹,

Yadav²,

Namasudra

et al. 2021

Int J Intell Syst

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Community detection algorithms (CDAs) are aiming to group nodes based on their connections and play an essential role in the complex system analysis. However, for privacy reasons, we may want to prevent communities or a group of nodes in the complex industrial network from being discovered in some instances, leading to the topics on community deception. In this paper, we introduce and formalize two intelligent community deception methods to conceal the nodes from various CDAs. We used node‐based matrices, persistence and safeness scores, to formalize the optimization problems to confound the CDAs. The persistence score is used to destabilize the constant communities in the network while the safeness score is used to assess the level of hiding of a node from CDAs. The objective functions aim to minimize the persistence score and maximize the safeness score of the nodes in the network. From the simulation results, it can be analyzed that the proposed strategies are intelligently concealing the community information in the complex industrial system.

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“…us, deciding which crop will best fit concerning chemical attributes is defined as an ill-posed problem [13]. en, agriculturists again want to develop crops to obtain the most ideal return and the least monetary hazard [14].…”

Section: Introductionmentioning

confidence: 99%

Optimal Crop Selection Using Gravitational Search Algorithm

Cheema

Singh

Gritli

2021

Mathematical Problems in Engineering

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For the economic growth of the crop, the optimal utilization of soil is found to be an open area of research. An efficient utilization includes various advantages such as watershed insurance, expanded biodiversity, and reduction of provincial destitution. Generally, soils present synthetic confinements for crop improvement. Therefore, in this paper, a novel diversified crop model is proposed to predict the suitable soil for good production of the crop. The proposed model utilizes a quantum value-based gravitational search algorithm (GSA) to optimize the best solution. Various features of soil are required to be investigated before crop selection. These features are refined further by applying quantum optimization. The crop selection based upon the soil requirement does not require any additional fertilizers which will reduce the production cost. Thus, the proposed model can select the optimal crop according to the soil components using the gravitational search algorithm. Therefore, the gravitational search algorithm is applied to the quantum values obtained from the crop and soil dataset. Extensive experiments show that the proposed model achieves an optimal selection of crops.

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Object tracking on event cameras with offline–online learning

Cited by 54 publications

References 33 publications

Flow‐assisted visual tracking using event cameras

Flow‐assisted visual tracking using event cameras

Intelligent deception techniques against adversarial attack on the industrial system

Optimal Crop Selection Using Gravitational Search Algorithm

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