Kalman Filter Based Motion Estimation for ADAS Applications

Bhat, Shreya; Kunthe, Sphoorti S.; Kadiwal, Vinuta; Iyer, Nalini C.; Maralappanavar, Shruti

doi:10.1109/icacccn.2018.8748616

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…In recent years classic as well as machine learning (ML) techniques have been used for pedestrian detection [13][14][15][16][17]. Authors in [13,16,17] discussed hybrid techniques for pedestrian detection. The major concern with these techniques is high computational complexity.…”

Section: Related Workmentioning

confidence: 99%

Computer vision-based framework for pedestrian movement direction recognition

Monika

Singh

Chand

2023

IFS

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Pedestrians are the most critical and vulnerable moving objects on roads and public areas. Learning pedestrian movement in these areas can be helpful for their safety. To improve pedestrian safety and enable driver assistance in autonomous driver assistance systems, recognition of the pedestrian direction of motion plays an important role. Pedestrian movement direction recognition in real world monitoring and ADAS systems are challenging due to the unavailability of large annotated data. Even if labeled data is available, partial occlusion, body pose, illumination and the untrimmed nature of videos poses another problem. In this paper, we propose a framework that considers the origin and end point of the pedestrian trajectory named origin-end-point incremental clustering (OEIC). The proposed framework searches for strong spatial linkage by finding neighboring lines for every OE (origin-end) lines around the circular area of the end points. It adopts entropy and Q measure for parameter selection of radius and minimum lines for clustering. To obtain origin and end point coordinates, we perform pedestrian detection using the deep learning technique YOLOv5, followed by tracking the detected pedestrian across the frame using our proposed pedestrian tracking algorithm. We test our framework on the publicly available pedestrian movement direction recognition dataset and compare it with DBSCAN and Trajectory clustering model for its efficacy. The results show that the OEIC framework provides efficient clusters with optimal radius and minlines.

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Section: Related Workmentioning

confidence: 99%

Computer vision-based framework for pedestrian movement direction recognition

Monika

Singh

Chand

2023

IFS

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“…Our experiments corroborate with these observations. It must be noted that recent developments in predictive filters can provide a more robust immunity against the noise present in systems like inertial navigation systems [29] and several ADAS applications [30]. However, at the same time, the principle that the UKF is more robust to non-linearities than the EKF remains true, as it is proven by recent advancements in UKF-based applications and its fine-tuning for autonomous vehicles, both conventional and otherwise [31,32].…”

Section: Trackers-ekf and Ukfmentioning

confidence: 99%

Switching Trackers for Effective Sensor Fusion in Advanced Driver Assistance Systems

Deo

Palade

2022

Electronics

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Modern cars utilise Advanced Driver Assistance Systems (ADAS) in several ways. In ADAS, the use of multiple sensors to gauge the environment surrounding the ego-vehicle offers numerous advantages, as fusing information from more than one sensor helps to provide highly reliable and error-free data. The fused data is typically then fed to a tracker algorithm, which helps to reduce noise and compensate for situations when received sensor data is temporarily absent or spurious, or to counter the offhand false positives and negatives. The performances of these constituent algorithms vary vastly under different scenarios. In this paper, we focus on the variation in the performance of tracker algorithms in sensor fusion due to the alteration in external conditions in different scenarios, and on the methods for countering that variation. We introduce a sensor fusion architecture, where the tracking algorithm is spontaneously switched to achieve the utmost performance under all scenarios. By employing a Real-time Traffic Density Estimation (RTDE) technique, we may understand whether the ego-vehicle is currently in dense or sparse traffic conditions. A highly dense traffic (or congested traffic) condition would mean that external circumstances are non-linear; similarly, sparse traffic conditions would mean that the probability of linear external conditions would be higher. We also employ a Traffic Sign Recognition (TSR) algorithm, which is able to monitor for construction zones, junctions, schools, and pedestrian crossings, thereby identifying areas which have a high probability of spontaneous, on-road occurrences. Based on the results received from the RTDE and TSR algorithms, we construct a logic which switches the tracker of the fusion architecture between an Extended Kalman Filter (for linear external scenarios) and an Unscented Kalman Filter (for non-linear scenarios). This ensures that the fusion model always uses the tracker that is best suited for its current needs, thereby yielding consistent accuracy across multiple external scenarios, compared to the fusion models that employ a fixed single tracker.

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Kalman Filter Based Motion Estimation for ADAS Applications

Cited by 2 publications

References 9 publications

Computer vision-based framework for pedestrian movement direction recognition

Computer vision-based framework for pedestrian movement direction recognition

Switching Trackers for Effective Sensor Fusion in Advanced Driver Assistance Systems

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