Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network
Authors: Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify the effectiveness of our tracking algorithm through extensive experiment on a challenging benchmark, where our method illustrates outstanding performance compared to the state-of-the-art tracking algorithms. This section describes our implementation details and experimental setting. The effectiveness of our tracking algorithm is then demonstrated by quantitative and qualitative analysis on a large number of benchmark sequences. |
| Researcher Affiliation | Collaboration | Seunghoon Hong1 MAGA33@POSTECH.AC.KR Tackgeun You1 YOUTK@POSTECH.AC.KR Suha Kwak2 SUHA.KWAK@INRIA.FR Bohyung Han1 BHHAN@POSTECH.AC.KR 1Dept. of Computer Science and Engineering, POSTECH, Pohang, Korea 2Inria WILLOW Project, Paris, France |
| Pseudocode | No | The paper describes its algorithm in prose and uses mathematical equations, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a link or statement confirming the availability of its own source code for the methodology described. It mentions using 'Caffe: An open source convolutional architecture' and using 'available source code to reproduce the results' from other papers, but not its own. |
| Open Datasets | Yes | To evaluate the performance, we employ all 50 sequences from the recently released tracking benchmark dataset (Wu et al., 2013). |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits (e.g., percentages or counts). It describes how training examples for the online SVM are generated during tracking but not a predefined dataset partitioning for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU or CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using the 'Caffe library (Jia, 2013)' but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | Yes | The CNN takes an image from sample bounding box, which is resized to 227 227, and outputs a 4096-dimensional vector from its first fullyconnected (fc6) layer as a feature vector corresponding to the sample. To generate target candidates in each frame, we draw N(= 120) samples... the threshold δ in Eq. (16) is set to 0.3. The number of observations m used to build generative model in Eq. (13) is set to 30. All parameters are fixed for all sequences throughout our experiment. |