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GIANLUCA MANCUSI
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Pubblicazioni
2025
- Monocular per-object distance estimation with Masked Object Modeling
[Articolo su rivista]
Panariello, Aniello; Mancusi, Gianluca; Haj Ali, Fedy; Porrello, Angelo; Calderara, Simone; Cucchiara, Rita
abstract
2024
- Is Multiple Object Tracking a Matter of Specialization?
[Relazione in Atti di Convegno]
Mancusi, Gianluca; Bernardi, Mattia; Panariello, Aniello; Porrello, Angelo; Cucchiara, Rita; Calderara, Simone
abstract
End-to-end transformer-based trackers have achieved remarkable performance on most human-related datasets. However, training these trackers in heterogeneous scenarios poses significant challenges, including negative interference - where the model learns conflicting scene-specific parameters - and limited domain generalization, which often necessitates expensive fine-tuning to adapt the models to new domains. In response to these challenges, we introduce Parameter-efficient Scenario-specific Tracking Architecture (PASTA), a novel framework that combines Parameter-Efficient Fine-Tuning (PEFT) and Modular Deep Learning (MDL). Specifically, we define key scenario attributes (e.g, camera-viewpoint, lighting condition) and train specialized PEFT modules for each attribute. These expert modules are combined in parameter space, enabling systematic generalization to new domains without increasing inference time. Extensive experiments on MOTSynth, along with zero-shot evaluations on MOT17 and PersonPath22 demonstrate that a neural tracker built from carefully selected modules surpasses its monolithic counterpart. We release models and code.
2023
- TrackFlow: Multi-Object Tracking with Normalizing Flows
[Relazione in Atti di Convegno]
Mancusi, Gianluca; Panariello, Aniello; Porrello, Angelo; Fabbri, Matteo; Calderara, Simone; Cucchiara, Rita
abstract
The field of multi-object tracking has recently seen a renewed interest in the good old schema of tracking-by-detection, as its simplicity and strong priors spare it from the complex design and painful babysitting of tracking-by-attention approaches. In view of this, we aim at extending tracking-by-detection to multi-modal settings, where a comprehensive cost has to be computed from heterogeneous information e.g., 2D motion cues, visual appearance, and pose estimates. More precisely, we follow a case study where a rough estimate of 3D information is also available and must be merged with other traditional metrics (e.g., the IoU). To achieve that, recent approaches resort to either simple rules or complex heuristics to balance the contribution of each cost. However, i) they require careful tuning of tailored hyperparameters on a hold-out set, and ii) they imply these costs to be independent, which does not hold in reality. We address these issues by building upon an elegant probabilistic formulation, which considers the cost of a candidate association as the negative log-likelihood yielded by a deep density estimator, trained to model the conditional joint probability
distribution of correct associations. Our experiments, conducted on both simulated and real benchmarks, show that our approach consistently enhances the performance of several tracking-by-detection algorithms.
2022
- First Steps Towards 3D Pedestrian Detection and Tracking from Single Image
[Relazione in Atti di Convegno]
Mancusi, G.; Fabbri, M.; Egidi, S.; Verasani, M.; Scarabelli, P.; Calderara, S.; Cucchiara, R.
abstract
Since decades, the problem of multiple people tracking has been tackled leveraging 2D data only. However, people moves and interact in a three-dimensional space. For this reason, using only 2D data might be limiting and overly challenging, especially due to occlusions and multiple overlapping people. In this paper, we take advantage of 3D synthetic data from the novel MOTSynth dataset, to train our proposed 3D people detector, whose observations are fed to a tracker that works in the corresponding 3D space. Compared to conventional 2D trackers, we show an overall improvement in performance with a reduction of identity switches on both real and synthetic data. Additionally, we propose a tracker that jointly exploits 3D and 2D data, showing an improvement over the proposed baselines. Our experiments demonstrate that 3D data can be beneficial, and we believe this paper will pave the road for future efforts in leveraging 3D data for tackling multiple people tracking. The code is available at (https://github.com/GianlucaMancusi/LoCO-Det ).