What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of study are Artificial intelligence, Computer vision, Object detection, Pedestrian detection and Pedestrian.
Artificial intelligence is frequently linked to Pattern recognition in his study.
His work in the fields of Image segmentation and Machine vision overlaps with other areas such as SIMD.
Dariu M. Gavrila has included themes like Interval tree and Pattern matching in his Object detection study.
His research investigates the connection between Pedestrian detection and topics such as Robustness that intersect with issues in Real-time operating system and Image retrieval.
Dariu M. Gavrila combines subjects such as Stereopsis, Real-time computing and Simulation with his study of Pedestrian.
His most cited work include:
- The Visual Analysis of Human Movement (1673 citations)
- Monocular Pedestrian Detection: Survey and Experiments (1003 citations)
- 3-D model-based tracking of humans in action: a multi-view approach (681 citations)
What are the main themes of his work throughout his whole career to date?
Dariu M. Gavrila mostly deals with Artificial intelligence, Computer vision, Object detection, Pattern recognition and Pedestrian detection.
As a member of one scientific family, Dariu M. Gavrila mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Pattern recognition and Probabilistic logic.
The concepts of his Computer vision study are interwoven with issues in Kalman filter and Pedestrian.
His Object detection study combines topics from a wide range of disciplines, such as Image resolution and Feature extraction.
The Pattern recognition study combines topics in areas such as Contextual image classification and Feature.
His work in Image processing covers topics such as Data set which are related to areas like Wavelet.
He most often published in these fields:
- Artificial intelligence (89.34%)
- Computer vision (68.85%)
- Object detection (21.31%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (89.34%)
- Computer vision (68.85%)
- Segmentation (9.02%)
In recent papers he was focusing on the following fields of study:
Dariu M. Gavrila spends much of his time researching Artificial intelligence, Computer vision, Segmentation, Representation and Object detection.
Dariu M. Gavrila works mostly in the field of Artificial intelligence, limiting it down to topics relating to Machine learning and, in certain cases, Time horizon.
His Image and Object study in the realm of Computer vision connects with subjects such as Benchmarking, Radar and Steering wheel.
His work carried out in the field of Representation brings together such families of science as Margin and Regularization.
His Benchmark study integrates concerns from other disciplines, such as Orientation, Pose and Code.
His Dynamic Bayesian network study incorporates themes from Probabilistic logic, Statistical model and Pattern recognition.
Between 2017 and 2021, his most popular works were:
- Human motion trajectory prediction: a survey: (113 citations)
- EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes (85 citations)
- Context-Based Path Prediction for Targets with Switching Dynamics (55 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Computer vision, Time horizon and Dynamic Bayesian network.
As part of his studies on Artificial intelligence, Dariu M. Gavrila frequently links adjacent subjects like Data domain.
His work on Recurrent neural network as part of general Machine learning research is frequently linked to Context based, Mode, Mean squared prediction error and Position, bridging the gap between disciplines.
His studies deal with areas such as Robotics and Cluster analysis as well as Computer vision.
His study in Time horizon is interdisciplinary in nature, drawing from both Probabilistic logic, Statistical model and Pattern recognition.
The study incorporates disciplines such as Control, Stereo camera, Real-time computing and Motion planning in addition to Dynamic Bayesian network.
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