|
|
Pedestrians Detection in Traffic Environment by Machine Learning
Tilgner, Martin ; Klečka, Jan (referee) ; Horák, Karel (advisor)
Tato práce se zabývá detekcí chodců pomocí konvolučních neuronových sítí z pohledu autonomního vozidla. A to zejména jejich otestováním ve smyslu nalezení vhodné praxe tvorby datasetu pro machine learning modely. V práci bylo natrénováno celkem deset machine learning modelů meta architektur Faster R-CNN s ResNet 101 jako feature extraktorem a SSDLite s feature extraktorem MobileNet_v2. Tyto modely byly natrénovány na datasetech o různých velikostech. Nejlépší výsledky byly dosaženy na datasetu o velikosti 5000 snímků. Kromě těchto modelů byl vytvořen nový dataset zaměřující se na chodce v noci. Dále byla vytvořena knihovna Python funkcí pro práci s datasety a automatickou tvorbu datasetu.
|
|
|
Moving Objects Detection in Video Sequences
Havelka, Jan ; Ševcovic, Jiří (referee) ; Španěl, Michal (advisor)
The topic of this thesis is the recognition and detection of moving object and persons in video sequence and in the static image. Designed application uses the combination of background model for movement detection, histograms of oriented gradients method for person recognition and Lucas-Kanade method for object tracking.
|
|
| |
|
|
Monitoring Pedestrian by Drone
Dušek, Vladimír ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
This thesis is focused on monitoring people in a video footage captured by drone. People are detected by trained model of detector RetinaNet. A feature vector is extracted for each detected person using color histograms. Identification of people is realized by comparing their feature vectors with respect to their distance in the frame. In the end the trajectories of all people are visualized in a panorama image. Accuracy of the trained RetinaNet detector on difficult validation data is 58.6 %. Error rate is partially reduced by the way of algorithm design for trajectory visualisation. It's not necessary to successfully detect person on every frame for correct visualization of its trajectories. At the same time, static objects which are detected as person but are not moving are not consider as people and are not visualized at all. There is a lot of algorithms dealing with people detection however only a few approaches are focused on detection people from an aerial footage.
|
|
|
Using Synthetic Data for Improving Detection of Cyclists and Pedestrians in Autonomous Driving
Kopčilová, Zuzana ; Musil, Petr (referee) ; Smrž, Pavel (advisor)
This thesis deals with creating a synthetic dataset for autonomous driving and the possibility of using it to improve the results of vulnerable traffic participants' detection. Existing works in this area either do not disclose the dataset creation process or are unsuitable for 3D object detection. Specific steps to create a synthetic dataset are proposed in this work, and the obtained samples are validated by visualization. In the experiments, the samples are then used to train the object detection model VoxelNet.
|
|
|
Pedestrian Detector Domain Shift Robustness Evaluation, And Domain Shift Error Mitigation Proposal
Zemčík, Tomáš
This paper evaluates daytime to nighttime traffic image domain shift on Faster R-CNNand SSD based pedestrian and cyclist detectors. Daytime image trained detectors are applied on anewly compiled nighttime image dataset and their performance is evaluated against detectors trainedon both daytime and nighttime images. Faster R-CNN based detectors proved relatively robust, butstill clearly inferior to the models trained on nighttime images, the SSD based model proved noncompetitive.Approaches to the domain shift deterioration mitigation were proposed and future workoutlined.
|
|
| |
|
|
Monitoring Pedestrian by Drone
Dušek, Vladimír ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
This thesis is focused on monitoring people in a video footage captured by drone. People are detected by trained model of detector RetinaNet. A feature vector is extracted for each detected person using color histograms. Identification of people is realized by comparing their feature vectors with respect to their distance in the frame. In the end the trajectories of all people are visualized in a panorama image. Accuracy of the trained RetinaNet detector on difficult validation data is 58.6 %. Error rate is partially reduced by the way of algorithm design for trajectory visualisation. It's not necessary to successfully detect person on every frame for correct visualization of its trajectories. At the same time, static objects which are detected as person but are not moving are not consider as people and are not visualized at all. There is a lot of algorithms dealing with people detection however only a few approaches are focused on detection people from an aerial footage.
|
|
|
Pedestrians Detection in Traffic Environment by Machine Learning
Tilgner, Martin ; Klečka, Jan (referee) ; Horák, Karel (advisor)
Tato práce se zabývá detekcí chodců pomocí konvolučních neuronových sítí z pohledu autonomního vozidla. A to zejména jejich otestováním ve smyslu nalezení vhodné praxe tvorby datasetu pro machine learning modely. V práci bylo natrénováno celkem deset machine learning modelů meta architektur Faster R-CNN s ResNet 101 jako feature extraktorem a SSDLite s feature extraktorem MobileNet_v2. Tyto modely byly natrénovány na datasetech o různých velikostech. Nejlépší výsledky byly dosaženy na datasetu o velikosti 5000 snímků. Kromě těchto modelů byl vytvořen nový dataset zaměřující se na chodce v noci. Dále byla vytvořena knihovna Python funkcí pro práci s datasety a automatickou tvorbu datasetu.
|
|
|
Moving Objects Detection in Video Sequences
Havelka, Jan ; Ševcovic, Jiří (referee) ; Španěl, Michal (advisor)
The topic of this thesis is the recognition and detection of moving object and persons in video sequence and in the static image. Designed application uses the combination of background model for movement detection, histograms of oriented gradients method for person recognition and Lucas-Kanade method for object tracking.
|
guest ::
