Cascaded CNN method for far object detection in outdoor surveillance

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Uittreksel

In maritime surveillance, detection of small ships and vessels located far away in the scene is of vital importance for behaviour analysis. Comparing to closely located objects, far objects are often captured in a smaller size and lack the adequate amount of details. Therefore, conventional detectors fail to recognize them. This paper proposes a CNN-based cascaded method for reliable detection of objects and more specifically vessels, located far away from a surveillance camera. The cascaded method improves small object detection accuracy by additional processing of the obtained candidate regions in their original resolution. The additional processing includes another detection iteration and a sequence of detection verification steps. Experimental results on our real-world vessel evaluation dataset reveal that the cascaded method increases the recall rate and F1- measurement by 13% and 12%, respectively. Another benefit is that the method does not require an adopter to change the model and architecture of the applied network. As an additional contribution, we provide a labeled maritime dataset to open public access.

TaalEngels
TitelProceedings - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018
RedacteurenRichard Chbeir, Gabriella Sanniti di Baja, Luigi Gallo, Kokou Yetongnon, Albert Dipanda, Modesto Castrillon-Santana
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's40-47
Aantal pagina's8
ISBN van elektronische versie978-1-5386-9385-8
DOI's
StatusGepubliceerd - 3 mei 2019
Evenement14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018 - Las Palmas de Gran Canaria, Spanje
Duur: 26 nov 201829 nov 2018

Congres

Congres14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018
LandSpanje
StadLas Palmas de Gran Canaria
Periode26/11/1829/11/18

Vingerafdruk

Processing
Ships
Cameras
Detectors
Object detection

Trefwoorden

    Citeer dit

    Ghahremani, A., Bondarev, E., & de With, P. H. N. (2019). Cascaded CNN method for far object detection in outdoor surveillance. In R. Chbeir, G. S. di Baja, L. Gallo, K. Yetongnon, A. Dipanda, & M. Castrillon-Santana (editors), Proceedings - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018 (blz. 40-47). [8706233] Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/SITIS.2018.00017
    Ghahremani, Amir ; Bondarev, Egor ; de With, Peter H.N./ Cascaded CNN method for far object detection in outdoor surveillance. Proceedings - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018. redacteur / Richard Chbeir ; Gabriella Sanniti di Baja ; Luigi Gallo ; Kokou Yetongnon ; Albert Dipanda ; Modesto Castrillon-Santana. Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 40-47
    @inproceedings{7687c60a380b41d3a2b37b81ce6a5d34,
    title = "Cascaded CNN method for far object detection in outdoor surveillance",
    abstract = "In maritime surveillance, detection of small ships and vessels located far away in the scene is of vital importance for behaviour analysis. Comparing to closely located objects, far objects are often captured in a smaller size and lack the adequate amount of details. Therefore, conventional detectors fail to recognize them. This paper proposes a CNN-based cascaded method for reliable detection of objects and more specifically vessels, located far away from a surveillance camera. The cascaded method improves small object detection accuracy by additional processing of the obtained candidate regions in their original resolution. The additional processing includes another detection iteration and a sequence of detection verification steps. Experimental results on our real-world vessel evaluation dataset reveal that the cascaded method increases the recall rate and F1- measurement by 13{\%} and 12{\%}, respectively. Another benefit is that the method does not require an adopter to change the model and architecture of the applied network. As an additional contribution, we provide a labeled maritime dataset to open public access.",
    keywords = "Cascaded CNNs, Far object detection, Maritime surveillance, Vessel detection",
    author = "Amir Ghahremani and Egor Bondarev and {de With}, {Peter H.N.}",
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    Ghahremani, A, Bondarev, E & de With, PHN 2019, Cascaded CNN method for far object detection in outdoor surveillance. in R Chbeir, GS di Baja, L Gallo, K Yetongnon, A Dipanda & M Castrillon-Santana (redactie), Proceedings - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018., 8706233, Institute of Electrical and Electronics Engineers, Piscataway, blz. 40-47, Las Palmas de Gran Canaria, Spanje, 26/11/18. DOI: 10.1109/SITIS.2018.00017

    Cascaded CNN method for far object detection in outdoor surveillance. / Ghahremani, Amir; Bondarev, Egor; de With, Peter H.N.

    Proceedings - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018. redactie / Richard Chbeir; Gabriella Sanniti di Baja; Luigi Gallo; Kokou Yetongnon; Albert Dipanda; Modesto Castrillon-Santana. Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 40-47 8706233.

    Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

    TY - GEN

    T1 - Cascaded CNN method for far object detection in outdoor surveillance

    AU - Ghahremani,Amir

    AU - Bondarev,Egor

    AU - de With,Peter H.N.

    PY - 2019/5/3

    Y1 - 2019/5/3

    N2 - In maritime surveillance, detection of small ships and vessels located far away in the scene is of vital importance for behaviour analysis. Comparing to closely located objects, far objects are often captured in a smaller size and lack the adequate amount of details. Therefore, conventional detectors fail to recognize them. This paper proposes a CNN-based cascaded method for reliable detection of objects and more specifically vessels, located far away from a surveillance camera. The cascaded method improves small object detection accuracy by additional processing of the obtained candidate regions in their original resolution. The additional processing includes another detection iteration and a sequence of detection verification steps. Experimental results on our real-world vessel evaluation dataset reveal that the cascaded method increases the recall rate and F1- measurement by 13% and 12%, respectively. Another benefit is that the method does not require an adopter to change the model and architecture of the applied network. As an additional contribution, we provide a labeled maritime dataset to open public access.

    AB - In maritime surveillance, detection of small ships and vessels located far away in the scene is of vital importance for behaviour analysis. Comparing to closely located objects, far objects are often captured in a smaller size and lack the adequate amount of details. Therefore, conventional detectors fail to recognize them. This paper proposes a CNN-based cascaded method for reliable detection of objects and more specifically vessels, located far away from a surveillance camera. The cascaded method improves small object detection accuracy by additional processing of the obtained candidate regions in their original resolution. The additional processing includes another detection iteration and a sequence of detection verification steps. Experimental results on our real-world vessel evaluation dataset reveal that the cascaded method increases the recall rate and F1- measurement by 13% and 12%, respectively. Another benefit is that the method does not require an adopter to change the model and architecture of the applied network. As an additional contribution, we provide a labeled maritime dataset to open public access.

    KW - Cascaded CNNs

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    KW - Vessel detection

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    BT - Proceedings - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018

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    Ghahremani A, Bondarev E, de With PHN. Cascaded CNN method for far object detection in outdoor surveillance. In Chbeir R, di Baja GS, Gallo L, Yetongnon K, Dipanda A, Castrillon-Santana M, redacteurs, Proceedings - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018. Piscataway: Institute of Electrical and Electronics Engineers. 2019. blz. 40-47. 8706233. Beschikbaar vanaf, DOI: 10.1109/SITIS.2018.00017