KIPMIKIPMI

Communications in Science and TechnologyCommunications in Science and Technology

Intelligent surveillance system (ISS) has received growing attention due to the increasing demand on security and safety. ISS is able to automatically analyze image, video, audio or other type of surveillance data without or with limited human intervention. The recent developments in sensor devices, computer vision, and machine learning have an important role in enabling such intelligent system. This paper aims to provide general overview of intelligent surveillance system and discuss some possible sensor modalities and their fusion scenarios such as visible camera (CCTV), infrared camera, thermal camera and radar. This paper also discusses main processing steps in ISS: background-foreground segmentation, object detection and classification, tracking, and behavioral analysis.

In this paper, general overview of intelligent surveillance systems has been presented.Such intelligent systems are promising to be implemented in various environments and applications.This paper also has discussed some possible sensor modalities and their fusion scenarios to improve the system performance.Numerous techniques have been proposed to tackle several main processing steps.background-foreground segmentation, object detection and classification, tracking, and behavioral analysis.Although several promising results have been obtained, further studies are needed for real implementation with more complex settings.

Berdasarkan penelitian sebelumnya, terdapat beberapa arah penelitian lanjutan yang menarik untuk dieksplorasi. Pertama, pengembangan metode segmentasi latar depan-latar belakang yang lebih robust terhadap perubahan pencahayaan dan kondisi lingkungan yang dinamis menjadi sangat penting. Hal ini dapat dicapai dengan menggabungkan berbagai modalitas sensor, seperti kamera visual dan data jarak, untuk membedakan objek yang bergerak dari perubahan latar belakang. Kedua, penelitian lebih lanjut mengenai analisis perilaku manusia dalam skenario yang lebih kompleks, termasuk interaksi antar individu dan penggunaan senjata, diperlukan untuk meningkatkan akurasi dan keandalan sistem pengawasan. Ketiga, eksplorasi teknik pembelajaran mendalam (deep learning) untuk deteksi dan klasifikasi objek, serta pelacakan objek dalam lingkungan yang padat, dapat membuka peluang baru untuk meningkatkan kinerja sistem pengawasan secara keseluruhan.

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