Deep Learning-Based Tracking

• Deep learning-based tracking is a methodology that uses neural networks, including CNNs, RNNs, and transformers, for object detection, association, and motion prediction in complex scenarios.
• It integrates multiple sensor cues and employs advanced architectures to enhance robustness against occlusion and improve trajectory estimation.
• The approach achieves rapid performance and scalability across diverse applications such as autonomous vehicles, robotics, and medical imaging.

Deep learning-based tracking refers to the family of algorithms that leverage deep neural architectures for the online or offline association of object hypotheses, state estimation, and trajectory reconstruction across sequential perceptual data. Over the past decade, these methods have redefined tracking-by-detection, model-based tracking, feature tracking, and the integration of disparate sensor modalities, yielding rapid improvements in tracking accuracy, robustness to occlusion, real-time performance, and scalability across heterogeneous domains including vision, robotics, radar, and IoT systems.

1. Core Methodologies and Taxonomy

The contemporary taxonomy of deep tracking is organized primarily around two axes: the tracking-by-detection paradigm with modular deep networks, and end-to-end tracking-by-query or sequence-to-sequence models that directly output tracks without explicit data association. The principal families, summarized and systematized in comprehensive surveys (Adžemović, 16 Jun 2025), include:

• Joint Detection and Embedding (JDE): Simultaneously learns object detection and appearance embeddings for track matching (e.g., FairMOT, JDE).
• Heuristic-Based Online Association: Relies on cascaded matching (IoU, confidence, spatial proximity) without additional learned affinity weights, exemplified by ByteTrack and its variants.
• Motion-Based Approaches: Augment or replace Kalman/linear filters with deep models (LSTM, Bi-LSTM, transformers) to predict future motion or correct association under complex kinematics (Cheng et al., 10 Jul 2024, Garcea et al., 2020).
• Affinity Learning/Graph Methods: Employ deep similarity networks—Siamese, vision transformers, GNNs—for learned association costs or global min-cost flow assignment.
• End-to-End (Tracking-by-Query): DETR-inspired transformer architectures (e.g., MOTR, MOTRv2) that maintain dynamic sets of detection and track queries, jointly regressing to detections and track Identities per frame, sometimes with soft/differentiable assignment (Adžemović, 16 Jun 2025, Pinto et al., 2022).

In model-based multi-object tracking, deep architectures such as transformers have been shown to match or outperform classic Bayesian random finite set and hypothesis-level filters—especially as association complexity or nonlinearity increases (Pinto et al., 2022).

2. Network Architectures for Tracking

Deep tracking frameworks utilize a broad spectrum of architectures, ranging from convolutional and recurrent modules to transformers:

• CNN Backbones: For extracting spatial features—VGG, ResNet, MobileNet, AlexNet-derived networks, often pretrained on ImageNet (Zgaren et al., 2020, Zhai et al., 2016, Wang et al., 2017, Parchami et al., 2021). For example, the "coarse-to-fine" tracker fuses deep CNN features (VGG-16 conv5) with discriminative correlation filters for joint translation/scale robust localization (Zgaren et al., 2020).
• RNNs/LSTM/GRU: Used to model temporal appearance variation, motion, and handle partial/occluded observations. Residual and dense LSTM skip-connect architectures mitigate vanishing gradient problems and enable accurate tracking over long temporal windows (Garcea et al., 2020, Cheng et al., 10 Jul 2024, Lim et al., 2021, Wu, 2022).
• Transformer Encoders/Decoders: Sequence-to-sequence or query-based transformer architectures excel in multi-object tracking with complex association and nonlinear state transition, as in MT3v2 and MOTRv2 (Adžemović, 16 Jun 2025, Pinto et al., 2022).
• Specialized Feature Trackers: For robust feature correspondence, deep cross-correlation architectures with spatial softmax and matchability heads have shown superior resilience in challenging domains, such as surgical or low-texture scenes (Parchami et al., 2021).

Architectural depth, skip or dense connections within recurrent layers, and auxiliary heads (e.g., for feature detection or trackability) are all exploited to balance feature reuse, temporal memory, and adaptability to abrupt appearance change (Garcea et al., 2020, Parchami et al., 2021, Wang et al., 2017).

3. Association, Affinity, and Data Fusion

Central to multi-object tracking is the association step: matching new detections to existing tracks. Deep learning drives advances both in affinity calculation and sensor fusion:

• Affinity Learning: Embedding-based costs (triplet, contrastive, or binary classification over detection pairs) are learned by networks such as MobileNets, vision transformers, or GNNs (Cheng et al., 10 Jul 2024, Adžemović, 16 Jun 2025). Distance metrics are typically Euclidean or cosine in embedding space, with hard thresholds or learned margins.
• Graph-Based Methods: Global min-cost flow/assignment, sometimes in a learned GNN space or with Sinkhorn soft assignment, supports offline/global optimization for dense scenes and non-causal linking (Adžemović, 16 Jun 2025).
• Sensor Fusion: Robustness to environmental variability and sensor outages is achieved by fusing appearance and motion cues from heterogeneous sensors (radar+camera (Cheng et al., 10 Jul 2024), RGB-D (Garon et al., 2017), ISAC comms+ranging (Wang et al., 1 Apr 2025)). Deep architectures combine feature-level (embedding concatenation), score-level (late fusion), or decision-level rules (e.g., radar for depth, camera for lateral localization).
• Occlusion Handling and Interpolation: Deep temporal models and carefully designed KalmanNet, Bi-LSTM, or RNN predictors mitigate track loss during occlusion and recover missed detections via learned or linear state interpolation (Sawhney et al., 2023, Cheng et al., 10 Jul 2024).

Data association is performed via the Hungarian algorithm (linear assignment) or min-cost flow, leveraging costs computed by learned or heuristic affinity functions (Kara et al., 2023, Adžemović, 16 Jun 2025).

4. Training Losses, Supervision, and Evaluation

Supervised and self-supervised deep trackers are trained via composite objectives, selected to match both detection and association quality:

• Detection/Localization Losses: Standard cross-entropy and regression (L1, Smooth-L1, IoU, GIoU, or Dice losses) for bounding box or segmentation map prediction (Zgaren et al., 2020, Sawhney et al., 2023).
• Embedding/Association Losses: Triplet, contrastive, and sometimes auxiliary softmax losses enforce discriminative, re-identifiable embeddings (Cheng et al., 10 Jul 2024, Adžemović, 16 Jun 2025).
• Tracking Losses: Multi-object trackers further include association/classification or differentiable assignment (e.g., via Sinkhorn), and some offline methods introduce global flow losses or contrastive consistency objectives (Adžemović, 16 Jun 2025, Pinto et al., 2022).
• Self-Supervised/Unsupervised Learning: Input dropout and prediction horizon masking drive RNNs to model motion and occlusion explicitly without access to ground-truth (Ondruska et al., 2016).
• Sensor/Noise Augmentation: Heavy domain-specific augmentation, such as spatial/temporal noise, occluder insertion, or synthetic trajectory generation, is critical for generalization, as shown in deep 6-DOF (Garon et al., 2017), MEMTrack (Sawhney et al., 2023), and ISACTrackNet (Wang et al., 1 Apr 2025).
• Evaluation Metrics: MOTA, MOTP, HOTA, AMOTA, AMOTP, frame detection rate (FDR), and per-class/intersection-over-union metrics are standard (Adžemović, 16 Jun 2025, Cheng et al., 10 Jul 2024, Sawhney et al., 2023, Kara et al., 2023).

Results across benchmarks demonstrate that learned appearance association and deep temporal models can approach or exceed classic Bayesian filters, notably improving robustness under occlusion, high density, or nonlinear/multimodal motion (Cheng et al., 10 Jul 2024, Pinto et al., 2022).

5. Applications Across Sensing Modalities and Domains

Deep tracking architectures are adapted to diverse application areas, encompassing object, feature, particle, and extended target tracking:

• Autonomous Vehicles and ADAS: Fusion of radar and vision features with Bi-LSTM motion predictors achieve robust MOTA/MOTP under occlusion and sensor outages (Cheng et al., 10 Jul 2024).
• Medical/Surgical Feature Tracking: Joint learning of patch-level detectability and trackability surpasses classical KLT/SIFT in feature-poor scenes (Parchami et al., 2021).
• Cell and Microrobot Tracking: Time-symmetric deep predictors outperform consecutive-frame or hand-engineered algorithms, handling non-causal associations and heavy artifacts in microscopy (Szabó et al., 2023, Sawhney et al., 2023).
• IoT and Embedded Systems: Resource-constrained pipelines combine traditional detection (HO-PBAS) and lightweight deep regression (GOTURN), running on edge devices at low power with real-time throughput (Blanco-Filgueira et al., 2018).
• Model-Based Bayesian Tracking: Deep transformers (MT3v2) and KalmanNet enable matching or superseding PMBM/GLMB filters in complex or nonlinear sensor regimes, at orders-of-magnitude faster inference (Pinto et al., 2022, Wang et al., 1 Apr 2025).
• Extended Target Tracking in ISAC: Sequence DNN-gru modules with denoising and Kalman-refinement stages achieve near-radar performance in communication-only scenarios (Wang et al., 1 Apr 2025).

The deployability of deep tracking systems depends on careful adaptation to real-world sensor noise, motion regimes, and computational constraints.

6. Current Benchmarks, Limitations, and Prospects

Extensive benchmarking (MOT17, MOT20, DanceTrack, SportsMOT, NuScenes, OTB, KITTI) demonstrates that:

• Heuristic association methods (e.g., ByteTrack, BoT-SORT) excel in high-density, linear-motion pedestrian datasets.
• Learned affinity and end-to-end architectures (MOTRv2, MOTIP, CoNo-Link) are dominant in highly non-linear motion regimes and under appearance homogeneity or occlusion (Adžemović, 16 Jun 2025).
• Real-time, low-power, embedded operation remains a strength of modular, shallow pipelines, but transformer or end-to-end approaches currently incur high computational overhead (Blanco-Filgueira et al., 2018, Adžemović, 16 Jun 2025).

Challenges persist in:

• Scaling high-accuracy, end-to-end trackers to real-time video.
• Minimizing data requirements for cross-domain and rare scenario generalization.
• Handling multi-camera, 3D, and sensor-fused long-term tracking.
• Achieving differentiable global association at nontrivial scales.

Future research is converging toward hybrid architectures fusing the efficiency of fast modular pipelines with the adaptability and generalization of fully learned differentiable associations, deep sequence models, and adaptive motion/measurement models (Adžemović, 16 Jun 2025).

---

In summary, deep learning-based tracking integrates spatial and temporal representation learning, end-to-end association modeling, and principled uncertainty handling for diverse tracking applications. By leveraging architectural innovations from CNNs, LSTM/GRU, transformers, and domain-aware losses and augmentations, state-of-the-art trackers continue to close the gap between heuristically tuned pipelines and the theoretically optimal—but previously intractable—complete Bayesian solutions, while expanding the frontiers of scalable, robust, and cross-domain tracking.