… Participants are not expected to develop novel object detection models. They are … For
example, if the class “shore” has two sub-classes lakeshore and seashore, then the top 2 …

… , the detection classifier is a multiclass subclass detection … , abnormal object detection enables
monitoring and tracking of the … improving our method further, such as installing the model …

… due to the fact that more parameters increase the chance of over-fitting… of manifold sub-classes,
Ye et al. [32] can robustly … network pre-trained on Citypersons to detection pedestrians on …

… for the purpose of detection of a major subclass of VRUs ie cyclists and pedestrians using
the … , and the CRCs help to enhance detection speed by rapidly removing false detections. …

… the direction of travel for the vehicles and pedestrians. The accuracy and validity of the …
improve its performance. Next, fine-tuning with state-of-the-art pre-trained detection models such …

… the pedestrian image (eg 32 × 32) to feed a pre-trained CNN … subclasses is not important
for the pedestrian detection task. … detectors trained using body part subclasses perform better …

… network built up on the pre-trained very deep convolutional network … In order to improve the
detection accuracy of the model for … (mean Average Precision) is the mean of AP in subclass. …

… The background class is divided into two subclasses: samples … or depicted person, using
pre-trained weights from ModelNet … Overall, our system performs better than the state of the art …

… the nature of classes and sub-classes. Where … pre-trained feature extractor over a new
trajectory, the right column shows estimated features from the tracker, leading to an improvement …

… The sub-classes within the person class indicate full body, … a model fit for new environments
instead of using a pre-trained … tracking result for classification, the accuracy would increase. …