Visualizing driving video in temporal profile

Kilicarslan, Mehmet; Zheng, Jiang Yu

doi:10.1109/ivs.2014.6856420

Cited by 19 publications

(11 citation statements)

References 18 publications

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“…where V z = dZ/dt and V y = 0 due to fixed Y of horizontal line. The T T C thus can be computed from (10) according to (11), which results the same T T C as for points.…”

Section: Vertical Flow Divergence Estimationmentioning

confidence: 99%

“…In a motion sensitive belt over the horizon in the video, we detect the horizontal zero-flow spots, and then monitor the scene divergence vertically in the crossing vertical zones in video frames to avoid the object recognition and tracking with bounding box. These steps are implemented efficiently in the motion profiles condensed from the belt and zones [11]. We compute dense horizontal motion and detect the horizontal zero-flow spots in the motion profile.…”

Section: Introductionmentioning

confidence: 99%

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Predict Vehicle Collision by TTC From Motion Using a Single Video Camera

Kilicarslan

Zheng

2019

IEEE Trans. Intell. Transport. Syst.

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The objective of this work is the instantaneous computation of Time-to-Collision (T T C) for potential collision only from the motion information captured with a vehicle borne camera. The contribution is the detection of dangerous events and degree directly from motion divergence in the driving video, which is also a clue used by human drivers. Both horizontal and vertical motion divergence are analyzed simultaneously in several collision sensitive zones. The video data are condensed to the motion profiles both horizontally and vertically in the lower half of the video to show motion trajectories directly as edge traces. Stable motion traces of linear feature components are obtained through filtering in the motion profiles. As a result, this avoids object recognition and sophisticated depth sensing in prior. The fine velocity computation yields reasonable T T C accuracy so that the video camera can achieve collision avoidance alone from the size changes of visual patterns. We have tested the algorithm for various roads, environments, and traffic, and shown results by visualization in the motion profiles for overall evaluation.M. Kilicarslan was with the

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“…where V z = dZ/dt and V y = 0 due to fixed Y of horizontal line. The T T C thus can be computed from (10) according to (11), which results the same T T C as for points.…”

Section: Vertical Flow Divergence Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predict Vehicle Collision by TTC From Motion Using a Single Video Camera

Kilicarslan

Zheng

2019

IEEE Trans. Intell. Transport. Syst.

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“…We can profile the driving environment into the spatial-temporal Motion Profile Images ( M P I ) [1,2]. These images are generated by averaging a region around the horizon to capture horizontal motion similar to Figure 1b.…”

Section: Introductionmentioning

confidence: 99%

Motion-aware vehicle detection in driving videos

KILIÇARSLAN,

TEMEL

2022

Turkish Journal of Electrical Engineering and Computer Sciences

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This paper focuses on vehicle detection based on motion features in driving videos. Long-term motion information can assist in driving scenarios since driving is a complicated and dynamic process. The proposed method is a deep learning based model which processes Motion Frame Image. This image merges both spatial (frame) and temporal (motion) information. Hence, the model jointly detects vehicles and their motion from a single image. The trained model on Toyota Motor Europe Motorway Dataset reaches 83% mean average precision (mAP). Our experiments demonstrate that the proposed method has a higher mAP than a tracking-based model. The proposed method runs real-time in driving videos which enables the model to be used in time-critical applications such as autonomous driving and advanced driving assistance systems.

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“…Driving behavior has been already investigated worldwide. Quantitative estimation of various elements of driving can be based, e.g., on information extracted from audio and video data [18,19], global positioning system (GPS) data [20], or Controller Area Network (CAN) data, i.e., the accelerator opening rate, brake pedal pressure, and steering wheel angle [21,22]. Mobile devices are applied to collect such data as well [23,24].…”

Section: Introductionmentioning

confidence: 99%

Discovering Speed Changes of Vehicles from Audio Data

Kubera

Wieczorkowska

Kuranc

et al. 2019

Sensors

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In this paper, we focus on detection of speed changes from audio data, representing recordings of cars passing a microphone placed near the road. The goal of this work is to observe the behavior of drivers near control points, in order to check whether their driving is safe both when approaching the speed camera and after passing it. The audio data were recorded in controlled conditions, and they are publicly available for downloading. They represent one of three classes: car accelerating, decelerating, or maintaining constant speed. We used SVM, random forests, and artificial neural networks as classifiers, as well as the time series based approach. We also tested several approaches to audio data representation, namely: average values of basic audio features within the analyzed time segment, parametric description of the time evolution of these features, and parametric description of curves (lines) in the spectrogram. Additionally, the combinations of these representations were used in classification experiments. As a final step, we constructed an ensemble classifier, consisting of the best models. The proposed solution achieved an accuracy of almost 95%, without mistaking acceleration with deceleration, and very rare mistakes between stable speed and speed changes. The outcomes of this work can become a basis for campaigns aiming at improving traffic safety.

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Visualizing driving video in temporal profile

Cited by 19 publications

References 18 publications

Predict Vehicle Collision by TTC From Motion Using a Single Video Camera

Predict Vehicle Collision by TTC From Motion Using a Single Video Camera

Motion-aware vehicle detection in driving videos

Discovering Speed Changes of Vehicles from Audio Data

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