Recursive-learning-based moving object detection in video with dynamic environment

Goyal, Kalpana; Singhai, Jyoti

doi:10.1007/s11042-020-09588-w

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Detecting objects in crowded scene, full occlusion and extreme illumination variation might be improved. Goyal and Singhai [14] used Gaussian mixture model with foreground matching. This algorithm detects slow moving objects effectively.…”

Section: Introductionmentioning

confidence: 99%

Multiple Object Detection in Surveillance Video With Domain Adaptive Incremental Fast RCNN Algorithm

Joy¹,

Vijayakumar²

2021

INDJCSE

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Object detection is one of the key steps in wide variety of surveillance-based applications. This paper introduces an incremental class based Fast RCNN multiple object detection method that can be apply in various domain of surveillance video analytics. The two major components of proposed method are class incremental learning component and domain adaptation component. Class incremental learning helps for incremental adding of object classes with existing object classes for Fast RCNN. Domain adaptation component part improves the detection even the object presents in cross domain. Here basic object classes can be trained initially and then can add new object class incrementally according to new class requirement or according to domain. Benchmark datasets COCO, PETS S2L1, CD2014, Visor dataset were used for training and testing. Experimental results were showed that the proposed approach performs better in video datasets that contains challenges such as illumination variation, presence of shadow, partial occlusion and dynamic background.

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Section: Introductionmentioning

confidence: 99%

Multiple Object Detection in Surveillance Video With Domain Adaptive Incremental Fast RCNN Algorithm

Joy¹,

Vijayakumar²

2021

INDJCSE

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A Locally Weighted Linear Regression-Based Approach for Arbitrary Moving Shaky and Nonshaky Video Classification

Halder,

Shivakumara,

Pal

et al. 2024

Int. J. Patt. Recogn. Artif. Intell.

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Classification and identification of objects are complex and challenging in pattern recognition and artificial intelligence if a shaky and nonshaky camera captures the videos at different distances during the day and nighttime. This work presents a model for classifying a given video as a static, uniform, or arbitrarily moving videos so that the complexity of the problem can be reduced. To avoid the threat of different distances between the objects and the camera, the proposed work introduces new steps for estimating the depth of the objects in the video frames. We explore locally weighted linear regression for feature extraction from depth information based on the notion that the regression line fits almost all the points for uniformity and does not fit for arbitrary moving. The extracted features are fed to a random forest classifier to classify static, uniform, or arbitrary moving video. The results on a large dataset, which includes videos captured day and night, show that the proposed method successfully classifies static, uniform and arbitrary videos with 0.86, 1.00 and 0.67 F-measures, respectively. Overall, our method obtains 87% accuracy for classification of static, uniform and arbitrary video, which is superior to the state-of-the-art methods.

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Detecting Moving Vehicles from Satellite-Based Videos by Tracklet Feature Classification

Chen

Ferreira

2022

Remote Sensing

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Satellite-based video enables potential vehicle monitoring and tracking for urban traffic management. However, due to the tiny size of moving vehicles and cluttered background, it is difficult to distinguish actual targets from random noise and pseudo-moving objects, resulting in low detection accuracy. In contrast to the currently overused deep-learning-based methods, this study takes full advantage of the geometric properties of vehicle tracklets (segments of moving object trajectory) and proposes a tracklet-feature-based method that can achieve high precision and high recall. The approach is a two-step strategy: (1) smoothing filtering is used to suppress noise, and then a non-parametric-based background subtracting model is applied for obtaining preliminary recognition results with high recall but low precision; and (2) generated tracklets are used to discriminate between true and false vehicles by tracklet feature classification. Experiments and evaluations were performed on SkySat and ChangGuang acquired videos, showing that our method can improve precision and retain high recall, outperforming some classical and deep-learning methods from previously published literature.

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Recursive-learning-based moving object detection in video with dynamic environment

Cited by 3 publications

References 17 publications

Multiple Object Detection in Surveillance Video With Domain Adaptive Incremental Fast RCNN Algorithm

Multiple Object Detection in Surveillance Video With Domain Adaptive Incremental Fast RCNN Algorithm

A Locally Weighted Linear Regression-Based Approach for Arbitrary Moving Shaky and Nonshaky Video Classification

Detecting Moving Vehicles from Satellite-Based Videos by Tracklet Feature Classification

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