Semantic Person Retrieval in Surveillance Using Soft Biometrics: AVSS 2018 Challenge II

Halstead, Michael; Denman, Simon; Fookes, Clinton; Tian, Yingli; Nixon, Mark S.

doi:10.1109/avss.2018.8639379

Cited by 12 publications

(21 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed approach uses AVSS 2018 challenge II [5] dataset for semantic person retrieval using soft biometrics. The dataset contains two tasks: (1) person re-identification (task-1) -identify a person using the semantic description from an image gallery and (2) surveillance imagery search (task-2) -localize a person using the semantic description in a given surveillance video.…”

Section: Dataset Overviewmentioning

confidence: 99%

“…DenseNet-169 training is accomplished using full body images for gender classification. AVSS 2018 challenge II [5], RAP [17], PETA [18], and DukeMTMC-reID [21] datasets are used to collect 1,45,386 full body images. Each image is resized to 350 × 140 resolution to preserve the spatial ratio of full body.…”

Section: Densenet-169 Training For Gender Classificationmentioning

confidence: 99%

“…The proposed algorithm achieves highest average IoU for 20 sequences {TS. 2,3,5,7,8,9,13,14,16,17,20,22,24,26,27,34,36,37, 38, 39} out of 41. The above sequences cover all the difficulty levels in the dataset.…”

Section: Performance Analysismentioning

confidence: 99%

“…Deep learning based methodologies are widely used for semantic description based person retrieval due to its efficient learning. Convolutional Neural Network (CNN) based algorithms are reported for person attribute recognition [10][11][12] as well as for person retrieval [5,[13][14][15][16]. Semantic Retrieval Convolution Neural Network (SRCNN) [10] retrieves the soft biometrics with recognition rate of 20.1% and 46.4% at rank-1 for one-shot and multi-shot identification respectively on SoBiR [8] dataset.…”

Section: Introductionmentioning

confidence: 99%

“…Halstead et al proposed a challenge (AVSS 2018 challenge II) [5] to rectify the above limitations. The challenge is expected to retrieve a person in surveillance video using a semantic query.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Visual appearance based person retrieval in unconstrained environment videos

Galiyawala

Raval

Dave

2019

Image and Vision Computing

View full text Add to dashboard Cite

Visual appearance-based person retrieval is a challenging problem in surveillance. It uses attributes like height, cloth color, cloth type and gender to describe a human. Such attributes are known as soft biometrics. This paper proposes person retrieval from surveillance video using height, torso cloth type, torso cloth color and gender. The approach introduces an adaptive torso patch extraction and bounding box regression to improve the retrieval. The algorithm uses fine-tuned Mask R-CNN and DenseNet-169 for person detection and attribute classification respectively. The performance is analyzed on AVSS 2018 challenge II dataset and it achieves 11.35% improvement over state-of-the-art based on average Intersection over Union measure.

show abstract

Section: Dataset Overviewmentioning

confidence: 99%