Introducing Scene Understanding to Person Re-Identification using a Spatio-Temporal Multi-Camera Model

Liu, Xin; Groot, Herman G.J.; Bondarev, Egor

doi:10.2352/issn.2470-1173.2020.10.ipas-095

Cited by 1 publication

(2 citation statements)

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“…In this function, the added value of database filtering was evaluated, based on the implied travel time (see Section 3.2 , G). This function can be directly applied to other object classes as well, such as persons or vehicles [ 25 ].…”

Section: Experimental Results: Re-identificationmentioning

confidence: 99%

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Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques

Zwemer

Groot

Wijnhoven

et al. 2021

Sensors

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This paper presents a camera-based vessel-speed enforcement system based on two cameras. The proposed system detects and tracks vessels per camera view and employs a re-identification (re-ID) function for linking vessels between the two cameras based on multiple bounding-box images per vessel. Newly detected vessels in one camera (query) are compared to the gallery set of all vessels detected by the other camera. To train and evaluate the proposed detection and re-ID system, a new Vessel-reID dataset is introduced. This extensive dataset has captured a total of 2474 different vessels covered in multiple images, resulting in a total of 136,888 vessel bounding-box images. Multiple CNN detector architectures are evaluated in-depth. The SSD512 detector performs best with respect to its speed (85.0% Recall@95Precision at 20.1 frames per second). For the re-ID of vessels, a large portion of the total trajectory can be covered by the successful detections of the SSD model. The re-ID experiments start with a baseline single-image evaluation obtaining a score of 55.9% Rank-1 (49.7% mAP) for the existing TriNet network, while the available MGN model obtains 68.9% Rank-1 (62.6% mAP). The performance significantly increases with 5.6% Rank-1 (5.7% mAP) for MGN by applying matching with multiple images from a single vessel. When emphasizing more fine details by selecting only the largest bounding-box images, another 2.0% Rank-1 (1.4% mAP) is added. Application-specific optimizations such as travel-time selection and applying a cross-camera matching constraint further enhance the results, leading to a final 88.9% Rank-1 and 83.5% mAP performance.

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Section: Experimental Results: Re-identificationmentioning

confidence: 99%

“…If not, it is removed from the set of most likely matches of the current query. In previous work [ 25 ], we also refined the set of most likely matches for a query based on scene understanding, such as possible paths between cameras and plausible transition times. Consequently, this results into a significant gain in performance.…”

Section: Related Workmentioning

confidence: 99%

Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques

Zwemer

Groot

Wijnhoven

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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Introducing Scene Understanding to Person Re-Identification using a Spatio-Temporal Multi-Camera Model

Abstract: Fast track article for IS&T International Symposium on Electronic Imaging 2020: Image Processing: Algorithms and Systems proceedings.

Cited by 1 publication

References 22 publications

Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques

Multi-Camera Vessel-Speed Enforcement by Enhancing Detection and Re-Identification Techniques

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