ViTBAT: Video tracking and behavior annotation tool

Biresaw, Tewodros A.; Nawaz, Tahir; Ferryman, James; Dell, Anthony I.

doi:10.1109/avss.2016.7738055

Cited by 52 publications

(23 citation statements)

References 23 publications

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“…Other options are crowd sourcing [38] or family members [4]. Depending on the annotation type, the effort can be reduced by, for example, interpolation between so called key frames [8]. Through the right strategy it is possible to reduce the annotation time significantly, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed method assists the user on a sequencebased level. Examples of other sequence-based methods are [2,7,8,31,44]. Bianco et al [7] combines several methods to semi-automatically propose annotations, while Biresaw et al [8] (iVAT) uses automated tracking and other computer vision methods together with interpolation for assisting manual annotation.…”

Section: Related Workmentioning

confidence: 99%

“…Examples of other sequence-based methods are [2,7,8,31,44]. Bianco et al [7] combines several methods to semi-automatically propose annotations, while Biresaw et al [8] (iVAT) uses automated tracking and other computer vision methods together with interpolation for assisting manual annotation. Manen et al [31] propose a semiautomatic annotation method for multi object tracking in video, where the user tracks the object with the cursor and these weak annotations are transformed to dense box trajectories.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Semi-Automatic Annotation of Objects in Visual-Thermal Video

Berg

Johnander

Gevigney

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Deep learning requires large amounts of annotated data. Manual annotation of objects in video is, regardless of annotation type, a tedious and time-consuming process. In particular, for scarcely used image modalities human annotation is hard to justify. In such cases, semi-automatic annotation provides an acceptable option. In this work, a recursive, semi-automatic annotation method for video is presented. The proposed method utilizes a state-of-the-art video object segmentation method to propose initial annotations for all frames in a video based on only a few manual object segmentations. In the case of a multi-modal dataset, the multi-modality is exploited to refine the proposed annotations even further. The final tentative annotations are presented to the user for manual correction. The method is evaluated on a subset of the RGBT-234 visual-thermal dataset reducing the workload for a human annotator with approximately 78% compared to full manual annotation. Utilizing the proposed pipeline, sequences are annotated for the VOT-RGBT 2019 challenge.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Semi-Automatic Annotation of Objects in Visual-Thermal Video

Berg

Johnander

Gevigney

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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show abstract

“…Crowdsourcing has the potential to speed up data set creation (Liu and Golparvar-Fard, 2015). Furthermore, video annotation through tracking (Vondrick and Ramanan, 2011;Biresaw et al, 2016) can improve the annotation process.…”

Section: Limitation On Data Sets For New Actionsmentioning

confidence: 99%

Capturing and Understanding Workers’ Activities in Far‐Field Surveillance Videos with Deep Action Recognition and Bayesian Nonparametric Learning

Luo

Yang

et al. 2018

Computer aided Civil Eng

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Recording workers’ activities is an important, but burdensome, management task for site supervisors. The last decade has seen a growing trend toward vision‐based activity recognition. However, recognizing workers’ activities in far‐field surveillance videos is understudied. This study proposes a hierarchical statistical method for recognizing workers’ activities in far‐field surveillance videos. The method consists of two steps. First, a deep action recognition method was used to recognize workers’ actions, and a new fusion strategy was proposed to consider the characteristics of far‐field surveillance videos. The deep action recognition method with the new fusion strategy has achieved the comparable performance (0.84 average accuracy) on the far‐field surveillance data set in contrast to the original method on the public data sets. Second, a Bayesian nonparametric hidden semi‐Markov model was innovatively used to model and infer workers’ activities based on action sequences. The latent states of the fitted Bayesian model captured workers’ activities in terms of state duration distributions and state transition distributions, which are indispensable for understanding workers’ time allocation. It has been preliminarily illustrated that the activity information learned by the Bayesian model possesses the potential to implement objective work sampling, personal physical fatigue monitoring, trade‐level health risk assessment, and process‐based quality control. Also, the limitations of this study are discussed.

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“…We manually annotated all the vehicles' positions and unique identity in three event sequences using the openly available video annotation tool called ViTBAT [40]. For annotation, the video was created from the binary events data file.…”

Section: Dataset and Benchmarkmentioning

confidence: 99%

Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System

Chen

Cao

Aafaque

et al. 2018

Journal of Advanced Transportation

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Neuromorphic vision sensor is a new passive sensing modality and a frameless sensor with a number of advantages over traditional cameras. Instead of wastefully sending entire images at fixed frame rate, neuromorphic vision sensor only transmits the local pixel-level changes caused by the movement in a scene at the time they occur. This results in advantageous characteristics, in terms of low energy consumption, high dynamic range, sparse event stream, and low response latency, which can be very useful in intelligent perception systems for modern intelligent transportation system (ITS) that requires efficient wireless data communication and low power embedded computing resources. In this paper, we propose the first neuromorphic vision based multivehicle detection and tracking system in ITS. The performance of the system is evaluated with a dataset recorded by a neuromorphic vision sensor mounted on a highway bridge. We performed a preliminary multivehicle tracking-by-clustering study using three classical clustering approaches and four tracking approaches. Our experiment results indicate that, by making full use of the low latency and sparse event stream, we could easily integrate an online tracking-by-clustering system running at a high frame rate, which far exceeds the real-time capabilities of traditional frame-based cameras. If the accuracy is prioritized, the tracking task can also be performed robustly at a relatively high rate with different combinations of algorithms. We also provide our dataset and evaluation approaches serving as the first neuromorphic benchmark in ITS and hopefully can motivate further research on neuromorphic vision sensors for ITS solutions.

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ViTBAT: Video tracking and behavior annotation tool

Cited by 52 publications

References 23 publications

Semi-Automatic Annotation of Objects in Visual-Thermal Video

Semi-Automatic Annotation of Objects in Visual-Thermal Video

Capturing and Understanding Workers’ Activities in Far‐Field Surveillance Videos with Deep Action Recognition and Bayesian Nonparametric Learning

Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System

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