Name
Abstract | ||
|---|---|---|
Applications like autonomous driving, surveillance, or any application that demands scene analysis requires object detection, semantic segmentation and instance segmentation. In this paper, we focus on the problem of detecting each instance of a specific category of objects, specifically persons. A novel method for object detection is proposed based on a deep counting model. The feature extractor of the deep counting model is extended with additional layers for segmenting specific instances. While the feature extractor of the deep counting model already focuses on the persons in the scene, the segmentation layers help to get a more accurate estimation of the foreground with persons and the instance segmentation is able to estimate separate instances of persons. Our proposed method outperforms other methods on the CUHK08 dataset with an Average Miss Rate (AMR) of 14% and on the PETS09 dataset with an AMR of 41%. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8682662 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Deep convolutional neural networks, deep counting models, detection, segmentation, multi-task network | Object detection,Market segmentation,Pattern recognition,Task analysis,Computer science,Segmentation,Feature extraction,Image segmentation,Artificial intelligence,Deep learning,Semantics | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanjukta Ghosh | 1 | 0 | 2.03 |
| Peter Amon | 2 | 201 | 23.28 |
| Andreas Hutter | 3 | 297 | 29.47 |
| André Kaup | 4 | 861 | 127.24 |