Object detection for video surveillance using the SSD approach
| Název práce v češtině: | Detekce objektů pro kamerový dohled pomocí SSD přístupu |
|---|---|
| Název v anglickém jazyce: | Object detection for video surveillance using the SSD approach |
| Klíčová slova: | detekce objektů, kamerový dohled, hluboké neuronové sítě, architektura SSD |
| Klíčová slova anglicky: | object detection, video surveillance, deep neural networks, SSD architecture |
| Akademický rok vypsání: | 2018/2019 |
| Typ práce: | diplomová práce |
| Jazyk práce: | angličtina |
| Ústav: | Katedra softwarového inženýrství (32-KSI) |
| Vedoucí / školitel: | doc. RNDr. Jakub Lokoč, Ph.D. |
| Řešitel: | skrytý - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 13.02.2019 |
| Datum zadání: | 13.02.2019 |
| Datum potvrzení stud. oddělením: | 21.02.2019 |
| Datum a čas obhajoby: | 10.06.2019 09:00 |
| Datum odevzdání elektronické podoby: | 07.05.2019 |
| Datum odevzdání tištěné podoby: | 10.05.2019 |
| Datum proběhlé obhajoby: | 10.06.2019 |
| Oponenti: | RNDr. Petr Božovský, CSc. |
| Zásady pro vypracování |
| Video surveillance has become an essential tool for monitoring human activities for purposes of security, market research, traffic management, etc. A constant need for observing and analyzing the growing number of video streams has created a demand for real-time automated systems for this task. Automated object detection delivers a possibility to use computing power for video analysis and provide notifications and summaries of interesting activities.
In this thesis, the student will examine and comprehensibly outline techniques used for object detection, focusing on approaches inspired by the Single Shot Detector (SSD). Designed detectors will focus on video surveillance, thus primarily detecting objects like people and vehicles. Specifically, the thesis will investigate multiple state-of-the-art image classification models in combination with the SSD approach for the bounding box prediction. The second goal is to identify a variant capable of real-time video analysis. The student will also analyze the possibilities of detector optimization for video surveillance purposes. |
| Seznam odborné literatury |
| Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. Vol. 1. IEEE, 2005.
Liu, Wei, et al. "SSD: Single shot multibox detector." European conference on computer vision (ECCV). LNCS, volume 9905, Springer, Cham, 2016. Goodfellow, Ian, et al. Deep learning. Vol. 1. Cambridge: MIT press, 2016. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas, NV, 2016, pp. 770-778. doi: 10.1109/CVPR.2016.90. Huang, Jonathan, et al. "Speed/accuracy trade-offs for modern convolutional object detectors." IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3296-3297, doi: 10.1109/CVPR.2017.351 |