Distributed Multi-Agent Video Fast-Forwarding (DMVF)

• DMVF is a distributed framework that uses consensus and reinforcement learning to selectively fast-forward multi-view videos by skipping redundant frames.
• It employs decentralized decision-making with minimal communication overhead, ensuring robust real-time event coverage in resource-constrained environments.
• Experimental evaluations show DMVF outperforms traditional single-agent and offline methods in terms of coverage and processing efficiency in surveillance and autonomous applications.

Distributed Multi-Agent Video Fast-Forwarding (DMVF) is a consensus-based framework designed for efficient collaborative fast-forwarding of multi-view video streams in real-time, resource-constrained multi-agent systems. The approach enables a network of agents, typically camera-equipped devices such as robots or surveillance units, to adaptively skip redundant or unimportant video frames while maximizing system-wide coverage of salient events. It leverages the redundancy in overlapping camera viewpoints and employs decentralized decision-making with minimal communication overhead.

1. Collaborative Multi-Agent Framework

DMVF organizes multiple agents—each associated with a unique camera and local computational resource—into an undirected communication graph $G=(V,E)$. Each agent is autonomous, observing its own stream, but periodically exchanges information with immediate neighbors, allowing for scalable collaboration without centralized control. The objective is to jointly maximize the coverage of important frames (critical scene events, as defined by application-specific ground truth) across all agents while minimizing system resource consumption (processing, communication, and storage).

Key components:

• Agent: Each camera operates a local agent with embedded reinforcement learning (RL) capabilities.
• Communication: Agents are connected via a sparse communication graph, typically implemented over wireless links, facilitating only neighbor-to-neighbor exchanges.
• Frame Summaries: Agents transmit compact frame feature summaries rather than raw videos, efficiently reducing communication load.

2. Reinforcement Learning-Based Local Fast-Forwarding

Each agent's frame selection process is formulated as a Markov Decision Process (MDP), where the agent learns to select a fast-forwarding strategy that balances efficiency and information coverage.

• State ($s_k$): Feature vectors extracted from the current video frame.
• Actions ($a_k$): Number of frames to skip (adjustable, determines temporal jump).
• Reward ($r_k$): Measures the trade-off between skipping uninformative frames and covering important ones.

The reward function is defined as: $r_k = -SP_k + HR_k$ where

$$SP_k = \frac{\sum_{i \in t_k} 1(l(i)=1)}{T} - \beta \frac{\sum_{i \in t_k} 1(l(i)=0)}{T}$$

$HR_k = \sum_{i=z-w}^{z+w} 1(l(i) = 1) \cdot f_i(z)$

with $SP_k$ penalizing skipped important frames, $HR_k$ rewarding landing near important frames, and $\beta$ a tunable parameter.

DMVF implements three action-strategy modes:

• Normal pace: Uses the standard reward.
• Slow pace: Discourages skipping via a sigmoidal modulation:

$$r_k(slow) = ( -SP_k + HR_k ) \cdot (1 - \frac{sigmoid(a_k)}{2})$$

• Fast pace: Encourages skipping:

$$r_k(fast) = ( -SP_k + HR_k ) \cdot (1 + \frac{sigmoid(a_k)}{2})$$

The RL policy $\pi(s_k)$ aims to maximize the expected discounted cumulative reward for each agent: $$\pi(s_k) = \arg\max_{a} E[R|s_k, a, \pi], \quad R = \sum_k \gamma^{k-1} r_k$$ where $\gamma$ is a discount factor.

3. Distributed Consensus of Frame Importance

After each adaptation period, agents exchange their selected frame summaries with neighbors and compute the importance of their local view with respect to the global scene, using both intra- and inter-agent frame similarity.

• Frame similarity:

$sim(x, y) = e^{-\alpha \|x-y\|_2}$

with $\alpha$ a scaling hyperparameter; $x, y$ are frame feature vectors.

• Agent-to-Agent similarity:

$$sim\_agent(v_i, v_j) = \frac{1}{|v_j|} \sum_{s=1}^{|v_j|} \max_{a} sim(p_s(v_j), p_a(v_i))$$

• Localized initial importance score for each agent $j$ as estimated by $i$:

$$x_{ij}^0 = \begin{cases} \frac{1}{|V_i|-1} \sum_{v_k \in V_i, k \neq j} sim\_agent (v_j, v_k) & \text{if } i = j \text{ or } (i, j) \in E \ 0 & \text{otherwise} \end{cases}$$

where $V_i$ is the set of agent $i$'s neighbors plus itself.

• Consensus Update: Agents perform a weighted aggregation step:

$$x_i = \frac{\sum_{j \in V_i} \frac{1}{n_j} x_{ji}^0}{\sum_{j \in V_i} \frac{1}{n_j}}$$

where $n_j$ is the degree of agent $j$.

• Maximal Consensus Iterations: Agents iteratively update a vectorized importance score,

$$\vec{x}_i[k] = \max(\vec{x}_i[k], \vec{x}_j[k]), \; \forall j \in V_i$$

for as many steps as the diameter of the communication graph. All agents converge to a global consensus vector $\vec{x}$ indicating the relative importance of each agent's view.

4. Adaptive Strategy Assignment

Upon consensus, each agent receives a global ranking of its view’s unique information contribution. Based on preset system constraints (e.g., how many agents may adopt each pacing strategy in a given period), strategies are assigned:

• Highest importance: Assigned slow pace (retain more frames).
• Lowest importance: Assigned fast pace (aggressive skipping).
• Intermediates: Assigned normal pace.

The system parameters $X/Y/Z$ (number of agents per strategy) enable flexible tuning between processing-resource consumption and event coverage without re-engineering.

5. Experimental Evaluation and Results

DMVF has been benchmarked on both real-world and simulated multiview video datasets:

• VideoWeb: Real surveillance, six cameras, synchronized, human-labeled important actions.
• CarlaSim: Simulated driving scenarios.

Baselines include:

• Random and uniform frame skipping
• Clustering (Online KMeans, Spectral Clustering)
• Offline sparse modeling (SMRS)
• Single-agent RL (FFNet)

Performance Metrics

• Coverage (%): Fraction of important events/frames captured by any agent.
• Processing Rate (%): Fraction of frames actually processed and transmitted.

Key Findings

Method	Coverage (%)	Processing Rate (%)
Random	50.78	4.20
Uniform	25.80	3.70
OK	50.21	100.0
SC	44.74	100.0
SMRS	42.36	100.0
FFNet	61.91	6.02
DMVF	65.87	5.06

• DMVF achieves higher important-frame coverage and lower processing rate compared to both FFNet (single-agent RL) and any offline/clustered baseline.
• DMVF allows fine-grained trade-off between efficiency and coverage by tuning agent strategy assignments.
• Maximal consensus achieves rapid convergence (4–5 iterations for 6 agents) with low communication overhead (1.4%–3.7% of raw video data).
• Robustness to communication topology is observed: coverage degrades gracefully with reduced agent connectivity.

6. Comparisons, Ablations, and Scalability

• Offline/Clustering Approaches require access to the entire video stream, are not real-time, and process every frame, making them unsuitable for deployed multi-agent systems with limited resources.
• Single-Agent RL (FFNet) does not coordinate with peers, resulting in redundant frames and lower global coverage.
• Consensus Variants: DMVF-DGD (distributed gradient descent), DMVF-EXTRA, and averaging/self-score consensus were evaluated. Maximal consensus provided the highest coverage and fastest convergence.
• Communication Cost: Only selected-frame summaries are exchanged, and the system is engineered to maintain high throughput (280–313 FPS across mixed hardware, including embedded devices).

7. Technical and Practical Implications

DMVF demonstrates that distributed consensus over localized frame summaries, combined with RL-based local strategy adaptation, enables multi-agent video systems to sustain high event coverage at very low data, computation, and communication cost. This is particularly relevant for robotics, surveillance, and sensor networks where efficiency, decentralization, and adaptability are primary requirements. DMVF's capacity for online adaptation, robustness against variable network topologies, and resource-aware operation make it well suited for real-world deployment in distributed camera networks, autonomous vehicle fleets, and collaborative robotics scenarios.

A plausible implication is that future multi-agent perception systems may further leverage DMVF principles to optimize joint scene understanding with even lower bandwidth, greater scalability, and strong resilience to node or link failures (Lan et al., 2023, Lan et al., 2020).