The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, and Short/Long-Term Re-Identification From Aerial Devices

Kumar, S. V. Aruna; Yaghoubi, Ehsan; Das, Abhijit; Harish, B. S.; Proença, Hugo

doi:10.1109/tifs.2020.3040881

Cited by 50 publications

(35 citation statements)

References 24 publications

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“…The INUS platform incorporates a variety of well-known and novel computer vision and image processing algorithms for the object tracking and detection tasks. To verify the utility and functionality of the employed algorithms, several experiments were performed by using selected free available video datasets associated with aerial views from RGB sensors: (i) OKUTAMA [31], (ii) UAV123 [32], (iii) UCF-ARG [33], (iv) VisDrone2019 [21], (v) AU-AIR [34], (vi) CARPK [35], (vii) PUCPR [35], (viii) UAVDT [36], (ix) OIRDS [37], (x) JEKHOR [38], (xi) OTCBVS-RGB [39], (xii) P-DESTRE [40], and from thermal sensors: (i) VIVID [41], (ii) LITIV2012 [42], (iii) OTCBVS-THERMAL [39], (iv) IRICRA [43]. Table 1 depicts sample views from each dataset.…”

Section: Datasets For Experiments and Trainingmentioning

confidence: 99%

The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets

et al. 2021

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Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become attractive for many applications in emergency operations. This paper proposes a multipurpose situational awareness platform by exploiting advanced on-board processing capabilities and efficient computer vision, image processing, and machine learning techniques. The main pillars of the proposed platform are: (1) a modular architecture that exploits unmanned aerial vehicle (UAV) and terrestrial assets; (2) deployment of on-board data capturing and processing; (3) provision of geolocalized object detection and tracking events; and (4) a user-friendly operational interface for standalone deployment and seamless integration with external systems. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potential of the proposed platform, and future directions for extension and optimization are presented.

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Section: Datasets For Experiments and Trainingmentioning

confidence: 99%

The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets

et al. 2021

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“…Recently, Kumar et al [155] released a UAV-based dataset for Pedestrian Detection, Tracking, Re-Identification, and Search (P-DESTRE) from aerial devices. It also contains full surveillance frame videos.…”

Section: Person Retrieval Datasetsmentioning

confidence: 99%

Person Retrieval in Surveillance Using Textual Query: A Review

Galiyawala¹,

Raval²

2021

Preprint

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Recent advancement of research in biometrics, computer vision, and natural language processing has discovered opportunities for person retrieval from surveillance videos using textual query. The prime objective of a surveillance system is to locate a person using a description, e.g., a short woman with a pink t-shirt and white skirt carrying a black purse. She has brown hair. Such a description contains attributes like gender, height, type of clothing, colour of clothing, hair colour, and accessories. Such attributes are formally known as soft biometrics. They help bridge the semantic gap between a human description and a machine as a textual query contains the person’s soft biometric attributes. It is also not feasible to manually search through huge volumes of surveillance footage to retrieve a specific person. Hence, automatic person retrieval using vision and language-based algorithms is becoming popular. In comparison to other state-of-the-art reviews, the contribution of the paper is as follows: 1. Recommends most discriminative soft biometrics for specific challenging conditions. 2. Integrates benchmark datasets and retrieval methods for objective performance evaluation. 3. A complete snapshot of techniques based on features, classifiers, number of soft biometric attributes, type of the deep neural networks, and performance measures. 4. The comprehensive coverage of person retrieval from handcrafted features based methods to end-to-end approaches based on natural language description.

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“…It consists of about 16,000 frames collected in realistic and challenging indoor and outdoor scenarios. Recently, in [ 173 ] the P-DESTRE dataset have been introduced. It provides the identity annotation of 256 individuals, flying from heights in the range of about 5–7 m, and across multiple days and different appearances.…”

Section: Low and Medium Heightsmentioning

confidence: 99%

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

et al. 2020

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The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed.

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The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, and Short/Long-Term Re-Identification From Aerial Devices

Cited by 50 publications

References 24 publications

The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets

The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets

Person Retrieval in Surveillance Using Textual Query: A Review

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

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