Multiclass object detection system in imaging sensor network using Haar-like features and Joint-Boosting algorithm

Vaidehi, V.; Fathima, A. Annis; Ramanathan, S.; Sameer, N.; Sagar, Salla

doi:10.1109/icrtit.2011.5972251

Cited by 8 publications

(4 citation statements)

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“…The conventional object detection approaches in VSNs used various types of background subtraction techniques [12] to recognize the moving objects in the camera node's field-of-view. In such methods, camera nods send the captured/difference image that include objects to the base station [13,14]. While object detection methods with the camera nodes involvement perform preprocessing tasks after background subtraction and send only the bounding box of the objects or the useful information of them into the network [16,17,18,19].…”

Section: Taxonomy Of Object Detection Approaches In Vsnsmentioning

confidence: 99%

“…To accelerate multi-object detection in VSNs with low training data, Vaidehi et al [14] have been proposed a method using Haar-like features [27] and Joint-Boosting algorithm [28]. The foundation of work is that each camera node has a background image and periodically captures the foreground images from its field-of-view.…”

Section: Multi-object Detection Using the Haar-like Featuresmentioning

confidence: 99%

“…The conventional object detection approaches in VSNs used various types of the background subtraction techniques [12] to recognize the difference between the background and foreground images. In such method, if the background subtraction result exceeds a pre-determined threshold, the camera node will detect moving objects within the monitoring area and sends the captured/difference image to the base station for recognition operations [13,14]. In visual sensor networks, the data communication cost is usually much higher than the image processing cost [15].…”

Section: Introductionmentioning

confidence: 99%

“…In background subtraction approaches for object detection in VSNs, each camera node subtracts the background and foreground images. If the difference between the background and foreground images exceeds a pre-determined threshold, the camera node will detect moving objects within its monitoring area and sends the foreground/difference image to the base station for recognition and classification operations [13,14].…”

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confidence: 99%

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Object detection is one of the major challenges in visual sensor networks (VSNs) which is set up in the monitoring applications. Many approaches proposed to solve the object detection problem in VSNs, considering diverse metrics such as reliability, energy consumption, detection accuracy and being realtime. In this paper, a survey on the object detection methods in visual sensor networks is presented for the first time. Furthermore, this paper classified the methods precisely. Two main object detection categories in VSNs that explored in this paper are conventional object detection methods and object detection approaches with the camera nodes involvement. To be more precise, presented survey promotes an overview of recent object detection methods' literature with their performance evaluation. Also, this research is challenging and the object detection issue in the visual sensor networks is open caused by differences in estimations and performance metrics. Therefore, the survey concludes with open research challenges.

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